Katarzyna Świrk
email: katarzyna.swirk@sorbonne-universite.fr

Magnus Rønning
email: magnus.ronning@ntnu.no

Monika Motak
email: motakm@agh.edu.pl

Patricia Beaunier
email: patricia.beaunier@sorbonne-universite.fr

Patrick Da Costa
email: patrick.da_costa@sorbonne-universite.fr

Teresa Grzybek
email: grzybek@agh.edu.pl

Ce-and Y-Modified Double-Layered Hydroxides as Catalysts for Dry Reforming of Methane: On the Effect of Yttrium Promotion

Keywords: dry methane reforming, double-layered hydroxides, cerium, yttrium

Ce-and Y-promoted double-layered hydroxides were synthesized and tested in dry reforming of methane (CH4/CO2 = 1/1). The characterization of the catalysts was performed using Xray fluorescence (XRF), X-ray diffraction (XRD), N2 sorption, temperature-programmed reduction in H2 (TPR-H2), temperature-programmed desorption of CO2 (TPD-CO2), H2 chemisorption, thermogravimetric analysis coupled by mass spectrometry (TGA/MS), Raman, and high-resolution transmission electron microscopy (HRTEM). The promotion with cerium influences textural properties, improves the Ni dispersion, decreases the number of total basic sites, and increases the reduction temperature of nickel species. After promotion with yttrium, the increase in basicity is not directly correlated with the increasing Y loading on the contrary of Ni dispersion. Dry reforming of methane (DRM) was performed as a function of temperature and in isothermal conditions at 700 °C for 5 h. For catalytic tests, a slight increase of the activity is observed for both Y and Ce doped catalysts. This improvement can of course be explained by Ni dispersion, which was found higher for both Y and Ce promoted catalysts. During DRM, the H2/CO ratio was found below unity, which can be explained by side reactions occurrence. These side reactions are linked with the increase of CO2 conversion and led to carbon deposition. By HRTEM, only multi-walled and helical-shaped carbon nanotubes were identified on Y and Ce promoted catalysts. Finally, from Raman spectroscopy, it was found that on Y and Ce promoted catalysts, the formed C is less graphitic as compared to only Ce-based catalyst.

Introduction

Dry reforming of methane (DRM) is considered as a promising technology of CO2 utilization by CH4. The process results in synthesis gas production, and the latter can be converted into important chemicals, such as methanol or long chain hydrocarbons, e.g., diesel [START_REF] Mark | Reaction kinetics of the CO2 reforming of methane[END_REF][START_REF] Pakhare | A review of dry (CO2) reforming of methane over noble metal catalysts[END_REF]. However, the DRM has not yet been launched on an industrial scale due to its high endothermicity and high temperature conditions of the process. From thermodynamics, it is well known that DRM starts to be favorable at ca. 700 °C [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF][START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF]. While the operational conditions impose some challenges, there is still lack of a proper catalyst for such process in terms of reactivity and stability.

Nickel-based catalysts have been widely studied, and they are considered to be a good alternative for expensive materials which contain noble metals. Moreover, Ni-catalysts were reported to be active and selective in dry reforming of methane, as presented in detailed by Seo et al. [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF].

However, the Ni-containing materials are known to suffer from deactivation due to sintering of the active phase, oxidation of nickel, and carbon formation [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF][START_REF] Chen | Deactivation during carbon dioxide reforming of methane over Ni catalyst: Microkinetic analysis[END_REF]. For the latter two main side reactions contribute the most, i.e., direct methane decomposition (Equation 1) and Boudouard reaction (Equation 2). CH4 = 2H2 + C ΔH= +74.9 kJ/mol (1)

2CO = CO2 + C ΔH= -172.4 kJ/mol (2)
This type of deactivation may be inhibited by choosing a proper support and/or addition of a promoter.

Double-layered hydroxides (DLHs), also known as hydrotalcites, can be described by the following formula [M II 1-xM III x(OH)2] x+ (A n-)x/n•mH2O, where M II and M III are divalent and trivalent cations of metals, respectively, A n-is an n-valent anion and m is the number of molecular water [START_REF] Cavani | Hydrotalcite-type anionic clays: Preparation, properties and applications[END_REF][START_REF] Dębek | A short review on the catalytic activity of hydrotalcite-derived materials for dry reforming of methane[END_REF]. In this structure, the Mg 2+ cations are partially substituted by Al 3+ cations resulting in a positive charge. The latter can be compensated by CO3 2-anions that are sandwiched between two hydrotalcite sheets. Upon calcination at appropriate temperature nickel oxides are formed in the periclase-like structure of Ni-DLHs. After the thermal treatment in hydrogen, nickel oxides are reduced to metallic nickel, which is located on the surface of the catalyst and can react with CH4 in DRM [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF]. This kind of catalysts were reported as active and selective in dry reforming of methane [START_REF] González | Dry reforming of methane on Ni-Mg-Al nanospheroid oxide catalysts prepared by the sol-gel method from hydrotalcite-like precursors[END_REF][START_REF] Lin | Carbon dioxide reforming of methane over Ni catalysts prepared from Ni-Mg-Al layered double hydroxides: Influence of Ni loadings[END_REF][START_REF] Touahra | Effect of the Ni/Al ratio of hydrotalcite-type catalysts on their performance in the methane dry reforming process[END_REF][START_REF] Dębek | Methane dry reforming over hydrotalcite-derived Ni-Mg-Al mixed oxides: The influence of Ni content on catalytic activity, selectivity and stability[END_REF], however, a significant improvement in their performance can be observed after promotion with metals, such as Ce, Zr, Y, La, or Zn [START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF][START_REF] Daza | Syngas production from CO2 reforming of methane using Ce-doped Ni-catalysts obtained from hydrotalcites by reconstruction method[END_REF][START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF][START_REF] Dębek | Catalytic activity of hydrotalcite-derived catalysts in the dry reforming of methane: On the effect of Ce promotion and feed gas composition[END_REF][START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF][START_REF] Niu | Effect of oxide additives on the hydrotalcite derived Ni catalysts for CO2 reforming of methane[END_REF][START_REF] Dębek | Promotion effect of zirconia on Mg(Ni,Al)O mixed oxides derived from hydrotalcites in CO2 methane reforming[END_REF][START_REF] Świrk | Dry reforming of methane over Zr-and Y-modified Ni/Mg/Al double-layered hydroxides[END_REF][START_REF] Liu | La-promoted Nihydrotalcite-derived catalysts for dry reforming of methane at low temperatures[END_REF].

Modification with ceria was described as beneficial in dry reforming of methane, due to oxygen storage capacity and redox properties of Ce [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF][START_REF] Dębek | Catalytic activity of hydrotalcite-derived catalysts in the dry reforming of methane: On the effect of Ce promotion and feed gas composition[END_REF][START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF][START_REF] Ainirazali | CO2 reforming of methane over Ni/Ce-SBA-15: Effects of Ce addition[END_REF][START_REF] Han | Natural gas reforming of carbon dioxide for syngas over Ni-Ce-Al catalysts[END_REF][START_REF] Koo | CeO2 promoted Ni/Al2O3 catalyst in combined steam and carbon dioxide reforming of methane for gas to liquid (GTL) process[END_REF]. According to Daza et al. [START_REF] Daza | Syngas production from CO2 reforming of methane using Ce-doped Ni-catalysts obtained from hydrotalcites by reconstruction method[END_REF], 3 wt.% of Ce had a positive effect on carbon removal, and it was reported as an optimal content in Ni-based hydrotalcites. Also, as observed by Dębek et al. [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF], less graphitic carbon was formed in Ce-modified Ni-DLHs, in favor of the amorphous carbon which can be gasified during DRM [START_REF] Dębek | Catalytic activity of hydrotalcite-derived catalysts in the dry reforming of methane: On the effect of Ce promotion and feed gas composition[END_REF]. In the other study of Daza et al. [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF], the co-precipitation with cerium nitrate (assuming different ratios of Al/Ce, i.e., 1.5, 4.0, 9.0 and 24.0) resulted in the best catalytic performance at 700 °C for the catalyst with the highest Al/Ce ratio.

Yttrium has also been reported as a promising promoter, especially in dry reforming of methane [START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF][START_REF] Świrk | Dry reforming of methane over Zr-and Y-modified Ni/Mg/Al double-layered hydroxides[END_REF][START_REF] Świrk | Syngas production from dry methane reforming over yttrium-promoted nickel-KIT-6 catalysts[END_REF][START_REF] Huang | Highly stable mesoporous NiO-Y2O3-Al2O3 catalysts for CO2 reforming of methane: Effect of Ni embedding and Y2O3 promotion[END_REF][START_REF] Li | Methane reforming with CO2 using nickel catalysts supported on yttria-doped SBA-15 mesoporous materials via sol-gel process[END_REF]. In our previous study we showed that Y (0.6-1.5 wt.%) increased specific surface area of Ni-DLHs, led to smaller Ni crystallites and enhanced dispersion of active sites as compared to the unpromoted catalyst [START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF]. Similar positive effects were reported in other Y-promoted materials tested in DRM [START_REF] Świrk | Dry reforming of methane over Zr-and Y-modified Ni/Mg/Al double-layered hydroxides[END_REF][START_REF] Świrk | Syngas production from dry methane reforming over yttrium-promoted nickel-KIT-6 catalysts[END_REF][START_REF] Huang | Highly stable mesoporous NiO-Y2O3-Al2O3 catalysts for CO2 reforming of methane: Effect of Ni embedding and Y2O3 promotion[END_REF][START_REF] Li | Methane reforming with CO2 using nickel catalysts supported on yttria-doped SBA-15 mesoporous materials via sol-gel process[END_REF][START_REF] Li | Alumina supported Ni and Co catalysts modified by Y2O3 via different impregnation strategies: Comparative analysis on structural properties and catalytic performance in methane reforming with CO2[END_REF]. Moreover, Zr-modified hydrotalcites doped with yttrium (0.2-0.6 wt.%) showed good catalytic performance [START_REF] Świrk | Dry reforming of methane over Zr-and Y-modified Ni/Mg/Al double-layered hydroxides[END_REF]. In this study, the promotion with 0.4 wt.% of Y led to the better activity and stability (as compared to the Y-free material) that was linked to the formation of solid solution of ZrO2-Y2O3, which enhanced reduction of bulk NiO. Furthermore, doping ceria with yttrium was reported to result in the enhanced number of oxygen vacancies and the oxygen mobility [START_REF] Munteanu | Temperatureprogrammed reduction of lightly yttrium-doped Au/CeO2 catalysts: Correlation between oxygen mobility and WGS activity[END_REF][START_REF] Burbano | Oxygen vacancy ordering and the conductivity maximum in Y2O3-doped CeO2[END_REF][START_REF] Wang | Study of ceria-supported nickel catalyst and effect of yttria doping on carbon dioxide reforming of methane[END_REF][START_REF] Guo | A Ni/CeO2-CDC-SiC catalyst with improved coke resistance in CO2 reforming of methane[END_REF]. Ions of Ce 3+ (0.97 Å) can be replaced by Y 3+ (1.04 Å) and form solid solution due to their similar ionic radii [START_REF] Fernández | New hydrotalcite-like compounds containing yttrium[END_REF]. According to Munteanu et al. [START_REF] Munteanu | Temperatureprogrammed reduction of lightly yttrium-doped Au/CeO2 catalysts: Correlation between oxygen mobility and WGS activity[END_REF] CeO2 impregnated with yttrium led to a higher ionic surface conductivity as compared to the co-precipitated sample with Y(NO3)3•6H2O.

To the best of our knowledge, no studies dealt with Ni/hydrotalcite Ce and Y promoted catalysts for DRM. Thus, herein, Ce-and Y-promoted catalysts were tested in dry reforming of methane (CH4/CO2 = 1/1) for the first time. A fixed amount of ceria (3 wt.%) was assumed, together with different concentrations of yttrium in each sample (0.2, 0.4 or 0.6 wt.%) and higher Al/Ce ratio (from 28 to 36). A set of different characterization methods was used, including X-ray fluorescence (XRF), X-ray diffraction (XRD), N2 sorption, temperature-programmed reduction in H2 (TPR-H2), temperature-programmed desorption of CO2 (TPD-CO2), H2 chemisorption, thermogravimetric analysis coupled by mass spectrometry (TGA/MS), Raman, and high-resolution transmission electron microscopy (HRTEM).

Results and Discussion

Physicochemical Features of the Studied Catalysts

XRD diffractograms of freshly synthesized double-layered hydroxides are presented in Figure 1. The XRD patterns show the sharp reflections for (003), (006), (009), (015), ( 110), (018), and (113) planes at 2θ ca. 11, 23, 35, 39, 46, 60, and 62° (ICOD 00-014-0191), respectively. Based on first three reflections, structural parameters a and c' are calculated (Table 1). The former describes the cationcation distance in the layers of hydrotalcite materials. This distance remained stable after promotion with cerium and cerium/yttrium. The c' relates to the distance between two layers plus one brucitelike sheet, and the values suggest the presence of CO3 2-(7.65 Å) and NO3 -(8.79 Å) ions [START_REF] Cavani | Hydrotalcite-type anionic clays: Preparation, properties and applications[END_REF][START_REF] Dębek | Catalytic activity of hydrotalcite-derived catalysts in the dry reforming of methane: On the effect of Ce promotion and feed gas composition[END_REF]. Additionally, a separate phase of ceria is revealed in XRD diffractograms (Figure 1). However, an existence of Ce(OH)3 or CeCO3OH cannot be excluded. The former arises from Ce 3+ , which oxidizes fast to Ce 4+ in H2O. Then it can form CeO2. The CeCO3OH was considered to be a precursor of cerium oxide upon precipitation in the presence of carbonates. In the study of Daza et al. [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF] the CeCO3OH was recorded only for the sample with higher cerium content (Al/Ce = 1.5), however, well dispersed cerium carbonate hydroxide could be also formed in the low-loaded cerium materials (with assumed Al/Ce = 4.0, 9.0, 24.0). This cerium deposition on the surface of the brucite-like layers can originate from its bigger ionic radius (Ce 3+ = 1.019 Å versus Mg 3+ = 0.86 Å, Al 3+ = 0.675 Å) [START_REF] Fernández | New hydrotalcite-like compounds containing yttrium[END_REF]. Since a modification with yttrium occurred through dry impregnation, it is expected that Y is deposited on the surface of DLHs, but no separate phase of Y2O3 was recorded in XRD patterns, which is probably due to its small content. Table 1 presents metal contents for the prepared catalysts as determined by XRF. The nickel content was found in the range of 20-25 wt.%, and for all Ce-containing catalysts the amount of cerium was very close to the one assumed during the synthesis step, i.e., 3 wt.%. The amount of introduced yttrium was also close to the assumed one. Ni 2+ /Mg 2+ molar ratio showed values lower than 0.33, especially in Ce-and Y-promoted catalysts. It suggests that Mg 2+ were only partially substituted by Ni 2+ . Al/Ce molar ratios calculated for the modified catalysts were in the range of 28-36, which is higher than those in the study of Daza et al. [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF].

Textural properties are presented in Table 1. The modification with cerium led to a decrease of specific surface area due to a partial blockage of the surface. Similar observations were reported for Ni-hydrotalcites promoted with Ce[EDTA] - [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF], and after co-precipitation with Ce(NO3)3•6H2O [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF].

Further modification with yttrium positively affected SBET resulting in an increase of this value. The pore volume and pore size remained almost unchanged. 009)) [START_REF] Dębek | Catalytic activity of hydrotalcite-derived catalysts in the dry reforming of methane: On the effect of Ce promotion and feed gas composition[END_REF]. 3 specific surface area calculated from the Brunauer-Emmett-Teller (BET) calculation method. 4 pore volumes derived from the Barrett, Joyner and Halenda (BJH) desorption method. 5 pore size distribution obtained from the Barrett, Joyner and Halenda (BJH) desorption method.

Reducibility, Basicity, Ni Dispersion, and Crystallite Size

Textural properties were examined for materials after calcination and after reduction in a mixture of 5 vol.% of H2 in Ar (Tables 1 and2). A significant decrease of SBET can be observed for all samples, as compared to the materials after calcination (Table 1). The highest decrease was observed for the sample modified only with ceria, similarly as reported by Han et al. [START_REF] Han | Natural gas reforming of carbon dioxide for syngas over Ni-Ce-Al catalysts[END_REF] for NiCeAl catalyst. Moreover, a decrease in the pore volumes and pore diameters was also observed, in contrast to the values obtained for the HT sample.

TPR-H2 profiles are presented in Figure 2. One broad reflection, arising from the reduction of NiO located in the layer of DLHs, is distinguishable at ca. 860 °C. Maximum temperature of peaks shifted towards higher values after promotion with Ce (829 versus 876 °C). After addition of 0.6 wt.% of Y the temperature shift is more pronounced (873, 864, 895 °C for HT Ce/Y0.2, HT Ce/Y0.4, and HT Ce/Y0.6, respectively) (Table 2). This shows that Ce-and Y-promotions lead to a decrease in reducibility, as confirmed by the uptake of hydrogen (Table 2). Additionally, less intense peaks were recorded at moderate temperatures. At 350 °C nickel oxides, weakly-bonded with surface of hydrotalcite are reduced, as presented in the profile for the HT catalyst. Also, in reducing atmosphere ceria helps to create an oxygen reservoir through the redox behavior of Ce 4+ /Ce 3+ [START_REF] Pakhare | A review of dry (CO2) reforming of methane over noble metal catalysts[END_REF]. Three species of ceria, with increasing reduction temperatures, were reported in literature, i.e., reduction of (i) surface oxygen at 400-420 °C, (ii) surface lattice oxygen at 450-600 °C, and (iii) total bulk reduction into Ce2O3 up to 880 °C [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF]. Their presence can be observed on the right-hand side of the Figure 2, however overlapping of the peaks arising from the species assigned as (i) and (ii), makes them impossible to distinguish. Moreover, the peaks registered at temperatures lower than 400 °C, may arise from reduction of either NiO weakly interacting with the surface of the catalyst or bulk NiO [START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF][START_REF] Świrk | Dry reforming of methane over Zr-and Y-modified Ni/Mg/Al double-layered hydroxides[END_REF][START_REF] Dębek | Influence of Ce/Zr molar ratio on catalytic performance of hydrotalcite-derived catalysts at low temperature CO2 methane reforming[END_REF]. Similar conclusions may be drawn for species (iii), which peaks can overlap with reduction of NiO derived from the structure. XRD patterns for the reduced samples are presented in Figure 3. Fluorite CeO2 phase was detected (ICOD 00-023-1048), together with the periclase mixed-oxides (ICOD 00-045-0946). The latter are typically formed upon calcination [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF][START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF]. The reduction in a mixture of H2/Ar resulted in metallic Ni (ICOD 01-087-0712) with crystallite sizes presented in Table 2. No increase of Ni 0 particle size was observed after the modification with cerium and cerium/yttrium, in contrast to the study of Daza et al. [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF], in which the co-precipitation with Ce led to the formation of free nickel oxides.

Table 2 shows the H2 consumption calculated for metallic nickel after TPR-H2. After the modification with Ce and Y a small increase of Ni 0 dispersion is observed, from 8.9 for HT to 11.5, 10.2, 11.6, and 11.5% for HT Ce/Y0.2, HT Ce/Y0.4, HT Ce/Y0.6, respectively. The enhanced dispersion was also observed in the previous studies on Y-promoted catalysts, and connected with improved catalytic behavior, since better dispersed active sites make them more available for the reaction with CH4 in DRM [START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF]. The crystallite sizes calculated from H2 chemisorption, assuming spherical Ni crystallites, are in good agreement with the results obtained by XRD. Figure 4 presents TPD-CO2 results for the catalysts after TPR-H2. One can note that ceria promotion led to a decrease in total basicity, whereas yttrium promotion (0.2 and 0.6 wt%) resulted in a significant increase of the number of basic sites. For the former, a similar effect was observed in the literature [START_REF] Dębek | Catalytic activity of hydrotalcite-derived catalysts in the dry reforming of methane: On the effect of Ce promotion and feed gas composition[END_REF][START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF]. In the current study, the sequence of the total basicity is: 63, 107, 111, 212, 286 µmol/g for HT Ce < HT < HT Ce/Y0.4 < HT Ce/Y0.2 < HT Ce/Y0.6 catalysts, respectively.

According to literature, three types of basic sites may be distinguished for DLHs, i.e., (i) 135-142 °C, (ii) 203-218 °C and (iii) 316-367 °C, ascribed to weak Brønsted basic sites (surface hydroxyl groups), medium strong (Lewis acid-base sites), and strong basic sites (Lewis basic sites associated with oxygen anions), respectively [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF][START_REF] Liu | La-promoted Nihydrotalcite-derived catalysts for dry reforming of methane at low temperatures[END_REF]. The modification with Y and Ce promoters resulted in the loss of the weakest basic sites (Figure 4), in favor of the medium and strong ones. According to Dębek et al. [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF] cerium favors the strong basicity. During DRM, strongly adsorbed CO2 will not react easily with CH4 (Le Chatelier's principle) and thus, the methane will decompose, instead of reacting with carbon dioxide. When the influence of yttrium on basicity is concerned, there is no straight correlation between the rising Y concentration and the number of basic sites. The amount of weak basic sites decreased with the Y loading. Thus, it is evident that 0.2 wt.% of yttrium considerably affects the amount of the medium basic sites, while Y of 0.4 wt.% hindered CO2 adsorption.

Catalytic Performance in Dry Methane Reforming

CH4 and CO2 conversions and H2/CO molar ratio in dry reforming of methane tests are presented in Figure 5 and Table 3. It may be seen that the promotion with cerium resulted in an enhanced conversion for both methane and carbon dioxide at the temperature range of 750-600 °C. Thus, the beneficial effect of Ce may be assigned to the increase of Ni 0 dispersion as reported elsewhere [START_REF] Rad | Sol-gel vs. impregnation preparation of MgO and CeO2 doped Ni/Al2O3 nanocatalysts used in dry reforming of methane: Effect of process conditions, synthesis method and support composition[END_REF][START_REF] Mierczynski | High active and selective Ni/CeO2-Al2O3 and Pd-Ni/CeO2-Al2O3 catalysts for oxy-steam reforming of methanol[END_REF]. In our study, the increase in the activity cannot be linked with the increased basicity, as proposed by Daza et al. [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF], but likely with the Ni dispersion which is slightly higher for HT Ce, HT Ce/Y0.4 and HT Ce/Y0.6 catalysts. Both CH4 and CO2 conversions were similar for HT Ce and the catalyst promoted with Y 0.4 wt.%. However, a decrease in activity was observed for HT Ce/Y0.6 catalyst, which had the highest basicity. In order to further examine the influence of Y promotion, isothermal tests were performed at 700 °C for 5 h for the best performing samples from the previous test: HT Ce, HT Ce/Y0.2 and HT Ce/Y0.4, and for the unmodified catalyst HT. The results are presented in Figure 6. The catalytic activity followed the sequence: HT < HT Ce/Y0.4 < HT Ce. Moreover, for HT Ce/Y0.2 catalyst, a constantly increasing conversions, for both CH4 and CO2, were observed. The lowest values of H2/CO were obtained for the unmodified catalyst (H2/CO = 0.90), whereas for both HT Ce/Y0.4 and HT Ce catalysts the values were respectively ca. 0.94 and ca. 0.97 after 300 min. Considering that the registered H2/CO molar ratio is stable from 50 min to 300 min, and the obtained values are always lower than unity, side reactions such as reverse water gas shift (CO2 + H2 = CO + H2O) may have occurred. It is to some extent, favored in the presence of Y. It is well known that Ni 0 is the active site for DRM [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF], and that the DRM reaction over Ni-based catalysts proceeds through two main steps: (i) the decomposition of methane takes place on Ni active site and (ii) the dissociate adsorption of carbon dioxide on the metal surface and metal-surface interface [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF][START_REF] Aramouni | Catalyst design for dry reforming of methane: Analysis review[END_REF]. During the first step of proposed mechanisms, a carbon on the Ni surface is formed as a product of CH4 decomposition (CH4 = C(s) + 2H2). This carbon atom can be then oxidized by atomic oxygen derived from CO2 in the reverse Boudouard reaction (CO2 + C = 2CO). [START_REF] Świrk | Yttrium promoted Ni-based double-layered hydroxides for dry methane reforming[END_REF]. When the excess amount of oxygen is in close proximity to the Ni 0 particles, it will lead to formation of NiO, further reduced again to Ni 0 due to presence of H2 in gas stream [START_REF] Seo | Recent scientific progress on developing supported Ni catalysts for dry (CO2) reforming of methane[END_REF]. Moreover, the promoting effect of Ce has been mainly ascribed to its large oxygen storage capacity, which favors the oxidation of the carbon deposits formed upon direct methane decomposition [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF][START_REF] Koubaissy | CO2 reforming of methane over Ce-Zr-Ni-Me mixed catalysts[END_REF]. The addition of Ce-species into the hydrotalcite structure was also found to promote the reducibility of the nickel species, and also resulted in the introduction of new strong basic sites (low-coordinated surface O 2-) and intermediatestrength basic sites (Lewis acid/base pairings), which increased the CO2 adsorption capacity of the catalysts [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF]. In our case, the Ce and Y addition does not enhance reducibility, but leads to higher Ni 0 dispersion. This agrees with some studies in which Ce 4+ as CeO2 was used as promotor [START_REF] Rad | Sol-gel vs. impregnation preparation of MgO and CeO2 doped Ni/Al2O3 nanocatalysts used in dry reforming of methane: Effect of process conditions, synthesis method and support composition[END_REF][START_REF] Li | Methane reforming with CO2 to syngas over CeO2-promoted Ni/Al2O3-ZrO2 catalysts prepared via a direct sol-gel process[END_REF]. All these factors have a positive effect on the catalytic activity of nickel-based catalysts in dry reforming of methane [START_REF] Rad | Sol-gel vs. impregnation preparation of MgO and CeO2 doped Ni/Al2O3 nanocatalysts used in dry reforming of methane: Effect of process conditions, synthesis method and support composition[END_REF][START_REF] Mierczynski | High active and selective Ni/CeO2-Al2O3 and Pd-Ni/CeO2-Al2O3 catalysts for oxy-steam reforming of methanol[END_REF]. The authors found that addition of CeO2 into monometallic nickel effectively improves the dispersion of Ni particles on the catalyst surface and enhances the stability of the catalyst. In the presence of both, yttrium oxide and cerium oxide, it was shown that the metalsupport interactions enhanced catalytic performance, due to the oxygen storage/transport characteristic of the support [START_REF] Metcalfe | Oxygen transfer between metals and oxygen-ion conducting supports[END_REF], or to the generation of active centers at the interface between metal and support [START_REF] Wang | Study of ceria-supported nickel catalyst and effect of yttria doping on carbon dioxide reforming of methane[END_REF][START_REF] Wang | Study of Sm2O3-doped CeO2/Al2O3-supported copper catalyst for CO oxidation[END_REF][START_REF] Silver | The role of lattice anion vacancies in the activation of CO and as the catalytic site for methanol synthesis over zirconium dioxide and yttria-doped zirconium dioxide[END_REF][START_REF] Dilara | TPD and HREELS investigation of the reaction of formic acid on ZrO2(100)[END_REF]. Thus, to take advantage of the metal-support synergistic effect in the treatment of CO2 reforming of CH4, nickel in interaction with ceria-yttria appears to be appropriate for developing a catalyst that is resistant to carbon deposition and that exhibits stable operation for extended periods of time as we found in Figure 6 for both Y and Ce doped catalysts. The characterization over spent catalysts (further described in the text) has shown that Ce was always together with Y, indicating the possible presence of CeO2-Y2O3 phase, which favors Ni 0 dispersion and limits the sintering and the carbon formation.

Characterization of the Spent Catalysts

Textural properties were examined for the spent catalysts after methane dry reforming tests at 700 °C for 5 h. As listed in Table 4, the specific surface area decreased after catalytic tests for the Cemodified sample as compared to unpromoted catalyst HT (125 compared to 108 m 2 /g). On the contrary, addition of yttrium caused a slight increase of SBET (139 m 2 /g for HTNi-Y0.2). The values are close to the ones obtained for the calcined materials (Table 1). 1 specific surface area calculated from the Brunauer-Emmett-Teller (BET) calculation method. 2 pore volumes derived from the Barrett, Joyner and Halenda (BJH) desorption method. 3 pore size distribution obtained from the Barrett, Joyner and Halenda (BJH) desorption method Figure 7 presents XRD patterns of the spent catalysts after DRM tests. The Ni crystallite size, carbon formation and changes in the crystallographic structure were examined and compared to the results obtained for the reduced materials (cp. Figure 3). For the spent catalysts, typical reflections of metallic nickel (ICOD 01-087-0712) and periclase-like mixed oxides can be found (ICOD 00-045-0946). Table 4 presents the calculated values of Ni 0 crystallite sizes, which did not change after the DRM tests. This proves that possible sintering of nickel was prevented after addition of ceria, confirming similar observations reported by Dębek et al. [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF]. On the other hand, no spinel phase was observed in contrast to the results shown by Daza et al. [START_REF] Daza | Co-precipitated Ni-Mg-Al catalysts containing Ce for CO2 reforming of methane[END_REF]. It should be mentioned, however, that additional reflections of hydrotalcite with the planes of ( 003), ( 006), (009) (ICOD 00-014-0191) were registered for the HT catalyst. Their presence suggests a partial reconstruction of the support in the presence of water (product). Although CH4 and CO2 conversions were stable, the formation of carbon was observed, as confirmed by the presence of graphite reflections at 2θ = 26.6° (ICOD 01-075-2078). The amount and the type of carbon formed on the catalysts upon the DRM were examined by TGA (Figure 8). The amount of carbon deposit was quantified the CO2 ion current detected during its combustion (Figure 9). During the thermal decomposition of the spent materials, a small decrease of the mass is registered in the temperature range of 40-300 °C, which is connected with water vapor removal (Figure 9). An increase of the mass is observed over 300 °C, which according to Tsyganok et al. [START_REF] Tsyganok | Dry reforming of methane over catalysts derived from nickel-containing Mg-Al layered double hydroxides[END_REF] arises from the oxidation of metallic nickel to nickel oxides. At 400 °C a significant decrease of the weight occurred due to the combustion of carbon deposits on the surface of the catalysts. In Figure 8, a CO2 formation is observed during the thermogravimetric analysis. For all catalysts, a peak centered at 610 °C corresponding to the carbon filaments is observed. The intensities of CO2 signals are in line with the amount of formed carbon, which were 1.97, 2.02, 2.53, 2.80 mg for HT Ce, HT, HT Ce/Y0.4, HT Ce/Y0.2, respectively. Raman spectra after isothermal tests are presented in Figure 10. For all samples four characteristic peaks located at 1473 cm -1 (D band), 1573 cm -1 (G band), 1602 cm -1 (D' band), and 2644 cm -1 (G' band) can be observed. The D-band is related to the disordered structural form of crystalline carbon species, whereas G-band is associated with graphitic carbon having high order, symmetry, and crystallinity. The degree of the carbon crystallinity (ID/IG) is shown in Table 3. The highest contribution of the graphitized carbon was observed for HT Ce catalyst, giving the lowest ID/IG ratio, i.e., 1.47. For the HT Ce/Y0.2 catalyst less graphitic carbon was observed than for HT Ce, despite the higher mass loss in the TGA analysis (cp. Figure 8). Thus, although the low loadings of yttrium (0.2 wt.%) led to the creation of higher amount of carbon, such deposits had lower graphitization degree. To gain more information on the structure of carbon deposits, high-resolution transmission electron microscopy analyzes were carried out for the samples after dry reforming of methane tests at 700 °C for 5 h. The micrographs of the spent HT Ce and HT Ce/Y0.4 catalysts are presented in Figure 11a,b. Helix-shaped carbon nanotubes are observed together with the high number of straight tubes, with well-defined graphite sheets with d-spacing of 3.3 Å for (111) planes. The helix-shaped nanotubes can be formed due to periodic incorporation of pentagon-heptagon pairs into a hexagonal carbon framework, generating curved surfaces [START_REF] Amelinckx | A formation mechanism for catalytically grown helix-shaped graphite nanotubes[END_REF]. Figure 11a additionally shows a high-resolution detail of tightly wound helix-shaped carbon nanotube containing 20-22 graphene sheets. Also, welldispersed cylindrical nickel particles of the mean size of 11-12 nm were registered. However, most of them were encapsulated with the carbon layers of dhkl = 3.35 Å (Figure 11). In HT Ce catalyst, cerium oxide particles were detected with distance of 3.2 Å (111) and the particle size of 7-13 nm. Some CeO2 particles formed aggregates in close vicinity to Ni. In case of the Y-modified sample, the particles of cerium oxide were single or together with small particles of yttrium (Figure 11b). Based on the recorded distance of 3.05 Å, it is hard to distinguish if a solid solution of CeO2-Y2O3 has been formed (dhkl = 3.12 Å for cubic) or two particles of cerium oxide and yttrium oxide are just neighboring each other (dhkl = 3.12 Å for cubic CeO2, dhkl = 3.06 Å for cubic Y2O3). This possible phase of yttriadoped ceria (YDC) can explain the high catalytic activity and better Ni 0 dispersion in agreement with literature [START_REF] Wang | Study of ceria-supported nickel catalyst and effect of yttria doping on carbon dioxide reforming of methane[END_REF].

Moreover, the support has been crystalized again in both samples, as confirmed by presence of the Mg and Al signals in EDS, and distance of dhkl = 2.52 Å probably arising from (009) plane. This is in the good agreement with the textural properties obtained for spent samples, i.e., value of SBET close to the one obtained for the calcined material.

Materials and Methods

Catalysts Synthesis

Double layered hydroxides, containing divalent M(II) and trivalent (III) ions (Ni 2+ , Mg 2+, Al 3+ and/or Ce 3+ ) were synthesized with assumed M(III)/M(II) molar ratio of 0.33. 3 wt.% was presumed in the Ce-containing samples. Co-precipitation method at constant pH = 9.8-10.2 was carried out with two aqueous solutions containing metal nitrates and sodium hydroxide (2 M) in the presence of sodium carbonate. The mixture was vigorous stirred at 65 °C. Afterwards, the solution was left to react for next 24 h at 65 °C, filtered and washed with deionized water. The solid cake was dried at 80 °C overnight. The freshly synthesized material was then divided into four portions and three of them were impregnated with yttrium nitrate, assuming Y content of 0.2, 0.4, or 0.6 wt.%. Finally, the dried materials were calcined at 550 °C for 5 h, and labelled as HT, HT Ce, HT Ce/Y0.2, HT Ce/Y0.4, and HT Ce/Y0.6.

Characterization Methods

Elemental analysis was carried out by Wavelength Dispersive X-Ray Fluorescence (WDXRF) using a Supermini200 instrument (Rigaku, Neu-Isenburg, Germany). Fifty milligrams of calcined materials were diluted in 2 g of boric acid and pelletized under a press (10 bar) for 15 min. The pellets were deposited in sample holders, and covered with 6 µm polypropylene film. The analyzes were carried out under vacuum at 36.5 °C in the presence of P-10 gas (a mixture of 10% CH4 in Ar with 24.7 cm 3 /min of flow). The quantities of metals were calculated by ZSX software (Rigaku, Neu-Isenburg, Germany).

Nitrogen sorption via a Micromeritics TriStar II 3020 (Micromeritics, Aachen, Germany) was used in order to verify the textural properties. The materials were degassed for 3 h at 110 °C before their subsequent measurements.

X-ray diffraction (XRD) patterns were obtained from PANalytical-Empyrean diffractometer (Malvern Panalytical B.V., Almelo, The Netherlands), equipped with CuKα (λ = 0.15406 nm) radiation source. From XRD, the medium crystallite size of nickel crystallite size was calculated based on the Scherrer equation.

In order to measure the reducibility of the Ni based double-layered hydroxides, temperatureprogrammed reduction (TPR-H2) experiments were carried out on a BELCAT-M (BEL Japan, Inc., Osaka, Japan) equipped with a thermal conductivity detector (TCD). Sixty milligrams of calcined were first degassed in helium atmosphere at 100 °C for 2 h and then reduced in 5% H2/Ar mixture with a heating rate of 10 °C/min starting from 100 °C to 900 °C. Temperature-programmed desorption (TPD-CO2) was performed after TPR-H2 run, using the same device. CO2 was adsorbed at 80 °C for 1 h from a mixture of 10% CO2/He. Then, helium flow was applied for 15 min in order to desorb weakly adsorbed CO2. Finally, the materials were heated from 80 °C to 800 °C in helium in order to determine the basic properties of the materials based on temperature of desorption. TPD profiles were deconvoluted into three Gaussian peaks corresponding to weak, medium and strong basic sites in agreement with literature [START_REF] Dębek | Ni-containing Cepromoted hydrotalcite derived materials as catalysts for methane reforming with carbon dioxide at low temperature-On the effect of basicity[END_REF][START_REF] Liu | La-promoted Nihydrotalcite-derived catalysts for dry reforming of methane at low temperatures[END_REF].

Hydrogen chemisorption was used to determine nickel dispersion using a Micromeritics, ASAP 2020S (Micromeritics, Aachen, Germany). Thus, 200 mg of calcined materials were loaded in tubular quartz reactor. Prior the experiments, evacuation at 40 °C for 1 h and reduction in pure H2 at 900 °C for 1 h was applied. After the reduction, the sample was evacuated in helium for 30 min at 900 °C and subsequently for 1 h at 40 °C. Then the leak test was performed. Prior to H2 chemisorption, the sample was evacuated for 30 min at 40 °C in helium. The metal dispersion was determined based on the quantity of hydrogen uptake. Moreover, from chemisorption technique, the crystallite size was also calculated, considering spherical metal crystallites with a uniform size [START_REF] Mustard | Determination of metal crystallite supported size and morphology supported nickel catalysts[END_REF].

Thermogravimetric analyzes were carried out by a TGA-MS (TGA: Netzsch STA 449C Jupiter, MS: Netzsch Aërlos QMS 403C, Netzsh Group, Selb, Germany) in order to examine the deposited coke on spent catalysts. A mixture of air (flow of 100 cm 3 /min) and protective gas (flow of 20 cm 3 /min) was applied. Ten milligrams of a spent sample was heated starting from room temperature to 900 °C with a heating rate of 10 °C/min. The amount of formed carbon was estimated by the mass loss in TGA analysis, confirming by CO2 production derived from MS results.

Raman measurements were performed by using a Horiba Jobin Yvon LabRam HR800 spectrophotometer (HORIBA, Kyoto, Japan) to characterize carbon deposition on the spent catalysts after DRM. The spectra were recorded in range of 3000-1000 cm -1 at room temperature. The excitation source was 633 nm of Ne-Ne laser. The spectra were recorded with an accumulation of 3 and an acquisition time of 20 s. A diffraction grating of 1800 g/mm and a 50× objective were applied. High-Resolution Transmission Electron Microscopy (JEOL JEM-2010, Croissy-sur -Seine, France with EDS) was used to characterize the deposited carbon and to obtain precise analysis of the planes registered at the nanometer scale. The specimens were prepared by dropwise addition of a colloidal solution in ethanol onto a copper grid covered with amorphous carbon film.

Catalytic Tests

The DRM tests for Ce-and Y-promoted catalysts were carried out in the fixed-bed quartz reactor heated inside an electric furnace. In each experiment 100-150 mg of calcined material was tested, depending on sample's density. The temperature of the catalyst bed was controlled with the aid of a K-type thermocouple. The total flow of feed gases was 100 cm 3 /min, corresponding to the GHSV of 20,000 h -1 . The products of the reaction were analyzed by an on-line Varian GC490 micro chromatograph equipped with a TCD detector. Prior to each catalytic test, a catalyst was reduced in situ with a mixture of 5% H2 in Ar for 1 h at 900 °C. Two types of tests were performed: (i) at the temperature range of 850-600 °C, (ii) isothermal test at 700 °C for 5 h.

Conclusions

The dry reforming of methane was studied over the double-layered hydroxides modified with cerium (co-precipitation method) and with yttrium (the incipient wetness impregnation method with 0.2, 0.4 and 0.6 wt.%). The promotion with both Ce and Y led to the decrease of reducibility of Ni, an increase of the basicity, enhanced Ni dispersion and smaller Ni crystallite size as compared to the HT catalyst. This can be explained by a possible formation of yttria-doped ceria (YDC) phase. Modification with the smallest loading of yttrium (0.2 wt.%) led to the increase of both CO2 and CH4 conversions during isothermal DRM tests for 5 hours. This enhancement of the activity is caused by side reactions. No clear deactivation was reported during the isothermal tests; however, formation of carbon was evident as spent samples were analyzed by TGA/MS, Raman and HRTEM. The amount of coke was higher for Y-promoted samples, although it was less graphitic as compared to the HT Ce catalyst.

Figure 1 .

 1 Figure 1. XRD patterns of freshly synthesized double-layered hydroxides (HT) catalysts: HT, HT Ce, HT Ce/Y0.2, HT Ce/Y0.4, HT Ce/Y0.6.

Figure 2 .

 2 Figure 2. Temperature-programmed reduction (TPR-H2) profiles recorded for Ce-and Y-modified catalysts. H2 consumption was recorded with the aid of a thermal conductivity detector (TCD).

Figure 3 .

 3 Figure 3. XRD diffractograms of the prepared catalysts after reduction in the mixture 5 vol.% of H2/Ar.

Figure 4 .

 4 Figure 4. TPD-CO2 plots for Ce-and Y-modified catalysts.

Figure 5 .Table 3 .

 53 Figure 5. The results of DRM catalytic tests over Ce-and Y-modified catalysts: (a) CH4 conversion, (b) CO2 conversion and (c) H2/CO molar ratio. Reaction conditions: T= 850-600 °C, 30 min at each temperature, GHSV = 20,000 h -1 , CH4/CO2/Ar = 1/1/8, total flow rate 100 cm 3 /min.

Figure 6 .

 6 Figure 6. The results of DRM catalytic tests over Ce-and Y-modified catalysts: (a) CH4 conversion, (b) CO2 conversion and (c) H2/CO molar ratio. Reaction conditions: T= 700 °C, 5 hours, GHSV = 20,000 h -1 , CH4/CO2/Ar = 1/1/8, total flow rate 100 cm 3 /min.

Figure 7 .

 7 Figure 7. XRD diffractograms recorded for the spent catalysts after isothermal tests at 700 °C for 5 h.

Figure 8 .

 8 Figure 8. Thermogravimetric analysis (TGA) for the catalysts after isothermal DRM tests.

Figure 9 .

 9 Figure 9. Mass spectrometry (MS) signals of water vapor and carbon dioxide during thermal decomposition of the spent catalysts.

Figure 10 .

 10 Figure 10. Raman spectra recorded for the spent catalysts.

Figure 11 .

 11 Figure 11. HRTEM micrographs of (a) HT Ce; and (b) HT Ce/Y0.4 catalysts after dry reforming of methane test at 700 °C for 5 h.

Table 1 .

 1 Structural parameters, elemental composition, and textural properties.

		XRD		XRF	N2 Sorption
	Catalyst	Freshly Synthesized	After Calcination	After Calcination
		a 1	c' 2
		[Å]	[Å]

Ni [wt.%] Ce [wt.%] Y [wt.%] Al/Ce [-] Ni 2+ /Mg 2+ [-] SBET 3 [m 2 /g] Vp 4 [cm 3 /g] dp 5 [nm]

	HT	3.06	7.82	20	-	-	-	0.29 (0.33)	120	0.6	19
	HT Ce	3.07	7.84	25	2.5 (3.0) *	-	34	0.21 (0.33)	97	0.5	20
	HT Ce/Y0.2	3.07	7.83	23	2.9 (3.0) *	0.3 (0.2) *	30	0.18 (0.33)	132	0.7	21
	HT Ce/Y0.4	3.07	7.84	23	3.2 (3.0) *	0.5 (0.4) *	28	0.18 (0.33)	133	0.6	19
	HT Ce/Y0.6	3.07	7.85	20	2.6 (3.0) *	0.7 (0.6) *	36	0.15 (0.33)	126	0.6	20

1 calculated from d-spacing a = 2d(110) as suggested by Cavani et al. [6]. 2 c' = c/3, where (c = d(003) + 2d(006) + 3d(

Table 2 .

 2 Texture, reducibility and Ni dispersion and Ni crystallite sizes for the studied materials after reduction in a mixture of 5 vol.% H2/Ar.

			N2 Sorption		TPR-H2	XRD	H2 Chemisorption
	Catalyst	Reduced Materials	H2	Ni	Ni	Ni
		SBET 1 [m 2 /g]	Vp 2 [cm 3 /g]	dp 3 [nm]	Consumption [mmol H2/g]	Crystallite [nm]	Dispersion [%]	Crystallite [nm]
	HT	68	0.4	21	0.209	9	8.9	11
	HT Ce	45	0.2	14	0.137	7	11.5	8
	HT Ce/Y0.2	99	0.7	28	0.135	7	10.2	10
	HT Ce/Y0.4	91	0.6	27	0.134	7	11.6	8
	HT Ce/Y0.6	99	0.7	26	0.119	8	11.5	8

Table 4 .

 4 Dry reforming of methane results at different temperatures.

	Catalyst

N2 Sorption XRD Raman SBET 1 [m 2 /g] Vp 2 [cm 3 /g] dp 3 [nm] Ni Crystallite Size [nm] ID/IG [-]

	HT	125	0.3	10	7	1.83
	HT Ce	108	0.4	13	7	1.47
	HT Ce/Y0.2	139	0.3	8	7	1.63
	HT Ce/Y0.4	120	0.4	12	7	1.51

specific surface area calculated from the Brunauer-Emmett-Teller (BET) calculation method.

pore volumes derived from the Barrett, Joyner and Halenda (BJH) desorption method.

pore size distribution obtained from the Barrett, Joyner and Halenda (BJH) desorption method

Conflicts of Interest:

The authors declare no conflict of interest.


Jalila Filali
email: jalila.filali@ensi-uma.tn

Hajer Baazaoui Zghal
email: hajerbz@yahoo.fr

Jean Martinet
email: jean.martinet@univ-lille.fr

Ontology and HMAX Features-based Image Classification using Merged Classifiers

Keywords: Image Classification, HMAX Features, Ontology

Bag-of-Viusal-Words (BoVW) model has been widely used in the area of image classification, which rely on building visual vocabulary. Recently, attention has been shifted to the use of advanced architectures which are characterized by multilevel processing. HMAX model (Hierarchical Max-pooling model) has attracted a great deal of attention in image classification. Recent works, in image classification, consider the integration of ontologies and semantic structures is useful to improve image classification. In this paper, we propose an approach of image classification based on ontology and HMAX features using merged classifiers. Our contribution resides in exploiting ontological relationships between image categories in line with training visual-feature classifiers, and by merging the outputs of hypernym-hyponym classifiers to lead to a better discrimination between classes. Our purpose is to improve image classification by using ontologies. Several strategies have been experimented and the obtained results have shown that our proposal improves image classification. Results based our ontology outperform results obtained by baseline methods without ontology. Moreover, the deep learning network Inception-v3 is experimented and compared with our method, classification results obtained by our method outperform Inception-v3 for some image classes.

INTRODUCTION

Image classification consists in labeling images with one of a number of predefined categories. To achieve this goal, machine learning techniques are used. As the basis of image processing and computer vision, image representation is the key study content in this field because its performance directly affects the image classification results. In this context, the Bagof-Visual-Words model (BoVW) proposed by [START_REF] Sivic | Video google: A text retrieval approach to object matching in videos[END_REF] is widely used in image classification and object recognition. In the literature, several works dealing with image classification revolve around BoVW method, which consist on building a visual vocabulary from image features [START_REF] Wang | How to use bag-of-words model better for image classification[END_REF], [START_REF] Gao | Image classification with bag-of-words model based on improved sift algorithm[END_REF]. The image features are quantified as visual words to express the image content through the distribution of visual words.

Recently, special attention has been shifted to the use of complex architectures which are characterized by multi-layers. Indeed, the biologically-inspired HMAX model was firstly proposed by [START_REF] Riesenhuber | Hierarchical models of object recognition in cortex[END_REF]. The HMAX model has attrac-ted a great deal of attention in image classification, due to its architecture which alternates layers of feature extraction with layers of maximum pooling. The HMAX model was optimized in the work of [START_REF] Serre | Robust object recognition with cortexlike mechanisms[END_REF] in order to add multi-scale representation as well as more complex visual features.

To improve image classification, several classification approaches based on ontologies have been proposed. The use of ontologies is generally motivated by the need to use semantic relations and describe data at a more semantic level for better classification. However, the accuracy of classification results remains far from authors' expectations due to several problems which still persist, such as the problem of ambiguity between classes. The ambiguity between classes is made explicit when similar visual features belonging to different image classes. An example of the ambiguity problem is presented in Figure 1.

In this paper, our objective is to propose an approach of image classification based on ontology and HMAX features using merged classifiers driven by taxonomic relationships in order to improve image classification. Our contribution consists in exploiting ontological relationships between classes in line with training visual-feature classifiers, and in merging the outputs of hypernym-hyponym classifiers to lead to a better discrimination between classes. The ontology is built to represent the semantic information associated with training images. The aim is to improve image classification using taxonomic relationships between image categories. The remaining parts of the paper are organized in the following way. Section 2 explains the related works and our motivations. Section 3 details our proposed image classification approach. In section 4, we present the experimental setup and then, we present and discuss the image classification performance. The last section concludes and recommends possible areas for future works.

RELATED WORK AND MOTIVATIONS

Image classification has acquired the attention of researchers in computer vision and image processing. In image classification, several methods and approaches have been introduced and applied. In this section, we give a general overview of the main related works. BoVW model was originally applied to classify images in the field of image processing and computer vision. Several studies have focused on the use of the BOVW-based methods for classifying and recognizing images. For instance, in [START_REF] Singhal | Image classification using bag of visual words model with fast and freak[END_REF] a novel technique of image classification using BOVW model is proposed. The aim is to perform bilinear classification of images, deciding between car and non-car images. The process involves feature detection of images using FAST features [START_REF] Rosten | Machine learning for high-speed corner detection[END_REF] and a supervised learning model is trained and then tested for image classification. The experiment shows that the proposed method is an efficient method of performing bi-linear classification.

Recently, several methods based on HMAX architecture have been used for improving image classification [START_REF] Theriault | Extended coding and pooling in the hmax model[END_REF], [START_REF] Hu | Sparsity-regularized hmax for visual recognition[END_REF]. In [START_REF] Theriault | Hmaxs: deep scale representation for biologically inspired image categorization[END_REF], a method of feature learning based on HMAX architecture for image classification has been proposed. The purpose is to build complex features with richer information to improve image classification. Moreover, in [START_REF] Zhang | B-hmax: A fast binary biologically inspired model for object recognition[END_REF] a fast binary-based HMAX model (B-HMAX) is proposed for object recognition. The goal is to detect cornerbased interest points and to extract few features with better distinctiveness. The idea is to use binary strings to describe the image patches extracted around detected corners, and then to use the Hamming distance for matching between two patches.

To improve image classification, several classification approaches based on ontologies have been proposed. For instance, [START_REF] Su | Improving image classification using semantic attributes[END_REF] proposed to address two limitations of BOVW model: the lack of explicit meanings of visual words and the visual words are usually polysemous. Two novel methods have been proposed to improve the performance of the bag-of-words model for image classification. Both approaches consist in predicting a set of semantic attributes. One is combining bag-of-words histograms with semantic image descriptors. The other is embedding semantic information into the visual vocabulary. In addition, [START_REF] Ristin | From categories to subcategories: large-scale image classification with partial class label refinement[END_REF] have addressed the problem of learning subcategory classifiers when only a fraction of the training data is labeled with fine labels while the rest only has labels of coarser categories. The aim is to adopt the framework of Random Forests [START_REF] Breiman | Random forests[END_REF] and to propose a regularized objective function that takes into account relations between categories and subcategories. Moreover, in [START_REF] Abdollahpour | Image classification using ontology based improved visual words[END_REF], a new visual vocabulary generation and feature representation method based on semantic taxonomies is proposed for image classification. The aim is, firstly, to leverage the semantic taxonomy to define visual words which are aware of contents and categories; secondly, to design a hierarchical classifier based on semantic taxonomies.

Other recent work tackle how coarse and fine labels can be used to improve image classification. In this context, [START_REF] Dutt | Improving image classification using coarse and fine labels[END_REF] address the problem of classification of coarse and fine grained categories by exploiting semantic relationships. In this work, the idea is to adjust the probabilities of classification according to the semantics of categories. An algorithm for doing such an adjustment is proposed to show the improvement for both coarse and fine grained classification. In [START_REF] Lei | Weakly supervised image classification with coarse and fine labels[END_REF], a weakly supervised image classification method with coarse and fine labels has been proposed. The goal is to use weakly labeled data aiming at learning a classifier to predict the fine labels during testing. For this, authors have proposed a CNN-based approach to address this problem, where the commonalities between fine classes in the same coarse class are captured by min-pooling in the CNN architecture.

Recently, deep learning and Convolutional Neural Networks (CNNs) have been successfully applied in many vision tasks including image classification and object recognition. The quality of network architectures significantly improved by utilizing deeper and wider networks such as Inception-v3 [START_REF] Szegedy | Rethinking the inception architecture for computer vision[END_REF] and AlexNet [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF]. [START_REF] Wu | Deep multiple instance learning for image classification and autoannotation[END_REF] incorporated deep learning into a weakly supervised learning framework and demonstrated that their deep multiple instance learning system achieves convincing performance in both image classification and image annotation. Moreover, (Wang et al., 2016a) proposed a unified CNN-RNN framework for multilabel image classification, which effectively learns both the semantic redundancy and the co-occurrence dependency in an end-to-end way. The multi-label RNN model learns a joint low-dimensional imagelabel embedding to model the semantic relevance between images and labels. In addition, [START_REF] Durand | Wildcat: Weakly supervised learning of deep convnets for image classification, pointwise localization and segmentation[END_REF]) introduced a deep learning method which jointly aims at aligning image regions for gaining spa-tial invariance and learning strongly localized features. The idea is to extend Convolution Neural Network at some levels for image classification.

To summarize the recent related work, we present in Table 1 a review of the related image classification approaches. It can clearly be seen that BoVW model has been applied by several work in both cases: without and with ontologies. But, in the literature, HMAX model is used only without ontologies for image classification. In addition, the use of ontologies is generally motivated by the need to improve image classification, however, the accuracy of classification results remain far from authors' expectations. Thus, despite this large number of the classification approaches in the literature, several problems persist, mainly the problem of ambiguity between classes that can degrade classification accuracy (cf.Figure 1). In previous work, an image annotation approach based on visual features and ontologies has been proposed [START_REF] Filali | Visually supporting image annotation based on visual features and ontologies[END_REF]. To achieve better image annotation precision, an improvement of image classification is needed.

To overcome the related work limitations, we propose an ontology and HMAX features-based image classification method using merged classifiers to improve image classification. Our motivation is to exploit ontological relationships between image categories in line with training visual-feature classifiers, and to merge the outputs of hypernym-hyponym classi-fiers in order to lead to a better discrimination between classes. The originality of our proposal concerns the integration of ontology in order to improve image classification and to decrease ambiguity between image classes.

In our method, firstly, we adopt HMAX model to extract visual features, and we build an ontology that can represent relationships between concepts (classes or categories) associated with training images. Secondly, visual-feature classifiers are trained according to taxonomic relationships between classes, in particular, for each super-category, images of subclasses are bagged in super-categories to train hypernym classifiers, and then for each sub-category, hyponym classifiers are trained using only images of the sub-classes in order to discriminate between the node's sub-categories. Finally, test images are classified using both hypernym and hyponym classifiers, then when the taxonomic relationship between the best hypernym class and the top hyponym classes are detected, output classifiers are merged to assign best classes to the test images. This enables to improve classification accuracy, and to decrease the ambiguity between classes.

ONTOLOGY AND HMAX FEATURES-BASED IMAGE CLASSIFICATION USING MERGED CLASSIFIERS

In this section, we describe our proposed image classification approach and detail their components. As depicted in Figure 2, our image classification approach is composed of three components: (1) feature extraction, (2) ontology building, and (3) image classification component. The different components are detailed below. Firstly, visual features are extracted from the training set (cf.Figure 2: (1) feature extraction). In our work, we adopted the HMAX model because HMAX features are generic, dos not require hand-tuning, and can represent well complex features with richer information (a detailed description is given below). Secondly, image categories from the train set are used to build the ontology which consists in establishing relations between classes using taxonomic relationships found in WordNet (cf.Figure 2: (2) ontology building). Finally, the obtained HMAX features are used to train classifiers. We have selected a multi-class linear SVM in order to classify images (cf.Figure 2: (3) image classification). The classification method is detailed in Section 3.3.

Feature Extraction

To extract visual features from training images, we use the HMAX model. In particular, we adopt the HMAX model to provide complex and invariant visual information and to improve the discrimination of features. The HMAX model follows a general 4layer architecture. We describe below the operations of each layer. Simple ("S") layers apply local filters that compute higher-order features and complex ("C") layers increase invariance by pooling units. A general architecture of the HMAX model is presented in Figure 3 [START_REF] Theriault | Hmaxs: deep scale representation for biologically inspired image categorization[END_REF].

• Layer 1 (S1 Layer): In this layer, each feature map is obtained from a convolution of the test image with a set of Gabor filters g s,o with orientations o and scales s. In particular, S1 Layer, at orientation o and scale s, is obtained by the absolute value of the convolution product given an image I:

L1 s,o = |g s,o * I| (1)
• Layer 2 (C1 Layer): The C1 layer consists in selecting the local maximum value of each S1 orientation over two adjacent scales. In particular, this layer partitions each L1 s,o features into small neighborhoods U i,j , and then selects the maximum value inside each U i,j .

L2 s,o = max U i,i ∈L1 s,o * U i, j (2)
• Layer 3 (S2 Layer): S2 layer is obtained by convolving filters α m , which combine low-level Gabor filters of multiple orientations at a given scale.

L3 m s = α m * L2 s (3)
• Layer 4 (C2 Layer): In this layer, L4 features are computed by selecting the maximum output of L3 m s across all positions and scales.

L4 = max (x,y),s L3 1 s (x, y), ..., max (x,y),s L3 M s (4)
The obtained C2 features (HMAX features) are used as the input of the multi-class linear SVM model to train classifiers.

Ontology Building

As depicted in Figure 2 : (2) ontology building, the ontology building component consists of two main steps, namely, concept extraction and relation generation. Let us consider that categories of the training images consisting of the synset (synonym set or concept that is described by one or multiple words) IDs which are defined by an external lexical resource; we used WordNet1 . To achieve this aim, firstly, we need to extract concepts (or classes) from the synsets. In particular, we extract the first concept that appears in this synset and relationships between concepts. We are interested in hypernymy and hyponymy relationships. Secondly, relationships between concepts are generated. The resulting sets of taxonomic relationships as well as the resulting concepts are the basis of our ontology. Finally, once the taxonomic relationships between concepts are extracted and created, the ontology is built. To successfully construct the ontology, we used the OWL API2 . Some rules are applied in order to transform the extracted relationships in OWL language. Algorithm 1 is used for building our ontology.

Image Classification

In this section, we detail our image classification method. As depicted in Figure 2 the obtained visual features and the ontology are used to perform the image classification. Visual features are used as the input of SVM classifiers. In our work, the aim is to learn a discriminative model for each class in order to predict the visual features membership. To achieve this goal, we focus on linear SVM classifiers since the diversity of image categories makes that using a non linear models is impractical. Given the visual features of the training images, we train a One-vs-All SVM classifier [START_REF] Cortes | Support-vector networks[END_REF] for each class to discriminate between this class and the other classes. Each classifier provide an output confidence value, which allows to determine the probability that a given image belongs to the related class.

In particular, the visual-feature classifiers are As depicted in Figure 4 (a), let us suppose that our ontology contains two hypernym concepts (supercategories) dog and cat and 4 hyponym concepts (subcategories) persian cat, tomcat under cat, and pooch, sheepdog under dog. At the given node domestic animal, its intermediate children are regarded as the super-categories. For example, the training images of cat will include the training image of persian cat and tomcat, thus the hypernym classifier of this supercategory is trained with these images. Then, for each hyponym concept (sub-category), we trained classifiers using their images separately. Finally, given, a test image, classification is done using both hypernym and hyponym classifiers as shown in Figure 4 (b). The test image is assigned to the class that has the best hypernym classifier that is dog in our example (because it gives the maximum confidence value). Also, it is assigned to the best hyponym classifier (tiger in our example) where classification is done using only the hyponym classifiers. As a result, top-k classes are obtained for each classification.

If the best hyponym class (tiger) has a direct relationship with the best hypernym class (dog), we merge the output of the hyponym-hypernym classifiers by combining their confidence values. For each selected hyponym class, a new confidence value is computed (is equal to the average of the confidence values of both hyponym and hypernym classes). The test image is assigned to the best hyponym class.

In the case where no direct relationship is detected between the best hyponym class and the best hypernym class, the next best hyponym classes from the top-k classes is considered and treated in the same manner. Therefore, cat and tomcat classifiers are selected because tomcat is the first hyponym class that appear in top k-classes and that has a direct relationship with cat class. The average of the confidence values obtained by the two selected classes is computed and is then assigned to tomcat class. This class becomes the correct class of the test image.

EXPERIMENTATION AND RESULTS ANALYSIS

Throughout this section, we illustrate the experimental results of our work. We start with the experimental setup, then, we present the evaluation of our method.

Experimental Setup

The aim is to evaluate the classification accuracy of our proposed method. To achieve this goal, we evaluated our approach on an animal dataset from Image-Net [START_REF] Deng | Imagenet: A large-scale hierarchical image database[END_REF]. We selected 22 animal classes from ImageNet, including 6 super-classes (cat, mammalian, bird, dog, insect and arachnid), 12 subclasses and 4 other sub-classes of carnivore and aquatic mammal categories. For each class, we used 180 images as training data and 20 images as testing images, resulting in a total 4400 images. We use Word-Net to generate a semantic ontology for the 22 animal categories. The resulting animal ontology is shown in Figure 5. We use accuracy as metric to evaluate the image classification results. In particular, for each class, the accuracy is computed as follows:

ACC = T P + T N N t (5)
Where: T P and T N is the number of true positive and true negative of images that were correctly classified, respectively, and N t is the total number of images.

Evaluation Results

The goal, in this experiment, is to show the effect and the advantages of using ontology to improve the image classification task. For this purpose, we compare our ontology-based image classification approach with the baseline method that consists in classifying images using SVM classification. Thus, to show better performance of our proposed image classification approach, we use different image classification strategies. We introduce the proposed strategies below:

1. BoVW: classical BoVW model is used with SVM.

2. BoVW-ONTO: same as above, plus ontology.

3. HMAX: HMAX features are extracted and classified with SVM.

4. HMAX-ONTO: same as above, plus ontology.

For the classification method based on BoVW model, SIFT features are extracted and quantized with KMeans and histograms of visual words are used to train SVM classifiers. In the case of the classification method that based on HMAX model, HMAX features are extracted as detailed in section 3.1.1 and they are used to train SVM classifiers. The size of the final features is, in the BoVW model, given by the size of the vocabulary. However, in the HMAX model, the size is given by the number of the C2 features. For both methods, multi-class classification is done using oneversus-all SVM.

For the classification strategies using ontology (HMAX-ONTO and BoVW-ONTO), features are extracted as previously. For classification, firstly, visualfeature classifiers are trained according to the ontology that describes the taxonomic relationships between image categories. Two ways of training classifiers are applied. In the first way, we trained classifiers which are associated with the super-categories (6 classes). Specifically, based on the taxonomic relationships, the training images of these categories include training images of their sub-classes. For example, training images for the super-category at the node bird include training images of wood ibis and robin. Thus, hypernyms classifiers are trained using images of both super and sub-categories. The second way consists in training classifiers which are associated with the sub-categories. In particular, for each class, we trained classifiers using their images separately. Thus, hyponyms classifiers are obtained using the sub-categories. If a class has children nodes in the ontology, the same process that is used in the first way is applied.

Given the test image, classification is done using both hypernym and hyponym classifiers. Firstly, the test image is assigned to the class that has the best hypernym classifier (that gives the maximum confidence value). Also, it is assigned to the best hyponym classifier where classification is done using only the hyponym classifiers. As a result, for each way, top If no direct relationship is detected between the best hyponym class and the best hypernym class, the next best hyponym class is considered and treated in the same manner. Classification results of some test images are presented in Figure 11. Figure 6 shows the comparison results in term of accuracy depending on the number of features for the BoVW and BoVW-ONTO methods. The obtained classification results for the strategy based on our method using BoVW model and ontology (BoVW-ONTO) is clearly higher than the strategy based on the BoVW model without ontology. Figure 7 shows the same comparison for HMAX features. It highlights a similar increase in accuracy when the ontology is used. The best accuracies for BoVW-ONTO and HMAX-ONTO methods were performed with a dictionary of 3000 features. In fact, as depicted in Table 2, the best improvement obtained by HMAX-ONTO method is 8%. However, the improvement reaches 12,63% for BoVW-ONTO method. According to the results, we conclude that We also focus on comparing the HMAX and BoVW models with and without ontology: HMAX versus BoVW (cf.Figure 8) and HMAX-ONTO versus BoVW-ONTO (cf.Figure 9). We observe in Figure 8 that, using SVM, classification method based on HMAX model provides a better performance than the classification method based on BoVW model. The best accuracy for HMAX method is obtained with a dictionary of 3000 features. When comparing the HMAX to BoVW method, the improvement reaches 11,66% (cf.Table 3). However, as depicted in Figure 9, we observe that, using the ontology, the difference in performance of classification results obtained by HMAX and BoVW models with ontology is much smaller and the best improvement reaches only 3,22% (cf.Table 3), and accuracy values are almost the same with a dictionary of 1000 features (cf.Figure 9). This results indicate that our proposed method, brings an increase in accuracy, independently of the selected features. Moreover, when comparing BoVW-ONTO and HMAX, we observe that the ontology helps the BoVW reach an accuracy (0.61) that is comparable to HMAX without ontology (0.60). Whereas without ontology, the accuracy of BoVW (0.42) is much lower than HMAX (0.46).

Finally, we compare our methods using ontology (BoVW-ONTO and HMAX-ONTO) with Inception-v3 model. The per-class accuracy is shown in Figure 10. Table 4 presents the accuracy comparison of some classes and the average accuracy of all used classes. We find that our methods with ontology outperform the Inception-v3 on some sub-classes such as pooch,

Figure 1 :

 1 Figure 1: Images correctly classified with positive (top) images that belong to tiger class and negative (bottom) images that, belong to cat class. The cat images appear in the tiger class due to their similar visual features to tiger images.

Figure 2 :

 2 Figure 2: Ontology and HMAX features-Based Image Classification Using Merged Classifiers.

Figure 3 :

 3 Figure 3: General architecture of the HMAX model (Theriault et al., 2011).

  : (3) image classification, Input : Cat I : categories, LR : lexical resource Output: θ: ontology 1 Initialization : θ ← (root : animal) 2 Concepts ← extractConcepts(Cat I ) 3 SubC ← f indHypoCon(root, concepts, LR) 4 While (|SubC| > 0

Figure 4 :

 4 Figure 4: Proposed image classification method.trained in two different strategies according to the taxonomic relationships between ontology concepts which represent image categories or classes. Firstly, at each node of the ontology, classes are bagged in super-categories based on the ontology in order to train hypernym classifiers. Specifically, based on semantic relationships, we can easily obtain training images for categories at each intermediate semantic level by grouping together images of their subcategories or hyponym concepts. Secondly, for each hyponym concept in the ontology, visual-feature classifiers are learned using sub-categories as labels and their training images.As depicted in Figure4(a), let us suppose that our ontology contains two hypernym concepts (supercategories) dog and cat and 4 hyponym concepts (subcategories) persian cat, tomcat under cat, and pooch, sheepdog under dog. At the given node domestic animal, its intermediate children are regarded as the super-categories. For example, the training images of cat will include the training image of persian cat and tomcat, thus the hypernym classifier of this supercategory is trained with these images. Then, for each hyponym concept (sub-category), we trained classifiers using their images separately. Finally, given, a test image, classification is done using both hypernym and hyponym classifiers as shown in Figure4 (b). The test image is assigned to the class that has the best hypernym classifier that is dog in our example (because it gives the maximum confidence value). Also, it is assigned to the best hyponym classifier (tiger in

Figure 5 :

 5 Figure 5: The ontology for the animal dataset.

Figure 6 :

 6 Figure 6: Comparison of accuracy depending on the number of features for BoVW and BoVW-ONTO methods.

Figure 7 :

 7 Figure 7: Comparison of accuracy depending on the number of features for HMAX and HMAX-ONTO methods. k-classes are obtained. Secondly, if the best hyponym class has a direct relationship with the best hypernym class, we merge their confidence values and the test image is assigned to the best hyponym class.If no direct relationship is detected between the best hyponym class and the best hypernym class, the next best hyponym class is considered and treated in the same manner. Classification results of some test images are presented in Figure11. Figure6shows the comparison results in term of accuracy depending on the number of features for the BoVW and BoVW-ONTO methods. The obtained classification results for the strategy based on our method using BoVW model and ontology (BoVW-ONTO) is clearly higher than the strategy based on the BoVW model without ontology. Figure7shows the same comparison for HMAX features. It highlights a similar increase in accuracy when the ontology is used. The best accuracies for BoVW-ONTO and HMAX-ONTO methods were performed with a dictionary of 3000 features. In fact, as depicted in Table2, the best improvement obtained by HMAX-ONTO method is 8%. However, the improvement reaches 12,63% for BoVW-ONTO method. According to the results, we conclude that

Figure 8 :

 8 Figure 8: Comparison of accuracy depending on the number of features for BoVW and HMAX methods. our ontology-based classification method with merged classifiers increases the classification accuracy for both HMAX and BoVW models. This explains that exploiting taxonomic relationships between images categories in line with training visual-feature classifiers and merging their out pouts classifiers, can improve the classification results.We also focus on comparing the HMAX and BoVW models with and without ontology: HMAX versus BoVW (cf.Figure8) and HMAX-ONTO versus BoVW-ONTO (cf.Figure9). We observe in Figure 8 that, using SVM, classification method based on HMAX model provides a better performance than the classification method based on BoVW model. The best accuracy for HMAX method is obtained with a dictionary of 3000 features. When comparing the HMAX to BoVW method, the improvement reaches 11,66% (cf.Table3). However, as depicted in Figure9, we observe that, using the ontology, the difference in performance of classification results obtained by HMAX and BoVW models with ontology is much smaller and the best improvement reaches only 3,22% (cf.Table3), and accuracy values are almost the same with a dictionary of 1000 features (cf.Figure9). This results indicate that our proposed method, brings an increase in accuracy, independently of the selected features. Moreover, when comparing BoVW-ONTO and HMAX, we observe that the ontology helps the BoVW reach an accuracy (0.61) that is comparable to HMAX without ontology (0.60). Whereas without ontology, the accuracy of BoVW (0.42) is much lower than HMAX (0.46).Finally, we compare our methods using ontology (BoVW-ONTO and HMAX-ONTO) with Inception-v3 model. The per-class accuracy is shown in Figure10. Table4presents the accuracy comparison of some classes and the average accuracy of all used classes. We find that our methods with ontology outperform the Inception-v3 on some sub-classes such as pooch,

Table 1 :

 1 Overview of the related image classification approaches.

	Approaches	Model	Ontology	Dataset
				JAFFE
	(Al Chanti and Caplier, 2018)	BoVW	-	DynEmo
	(Durand et al., 2017)	FCN ResNet-101	-	VOC 2012
	(Dutt et al., 2017)	CNN		CIFAR-100
	(Singhal et al., 2017)	BoVW	-	car images
	(Lei et al., 2017)	CNN		CIFAR-100
	(Zhang et al., 2016)	HMAX	-	GRAZ01
	(Szegedy et al., 2016)	Inception-v3	-	ILSVRC 2012
				NUS-WIDE
	(Wang et al., 2016a)	CNN-RNN	-	MS-COCO
				Caltech 101
	(Wang et al., 2016b)	BoVW	-	VOC 2007
	(Li et al., 2015)	HMAX	-	Caltech 101
	(Abdollahpour et al., 2015)	BoVW		CIFAR-10
	(Hu et al., 2014)	HMAX	-	Caltech 101
	(Gao et al., 2013)	BoVW	-	specific data
	(Su and Jurie, 2012)	BoVW		VOC 2007
	(Theriault et al., 2011)	HMAX	-	Caltech 101

https://wordnet.princeton.edu/

http://owlapi.sourceforge.net/

spider and tomcat. Accuracy values are almost the same for some super-classes such as aquatic mammal and dog. For all classes, the average accuracy obtained by HMAX-ONTO method is a little better than that is obtained by Inception-v3 model. This latter is also a little better than BoVW-ONTO method (cf.Table 4).

CONCLUSION

In this paper, an image classification approach has been defined. It relies on training visual-feature classifiers according to the taxonomic relationships be-tween image categories. Firstly, visual features are extracted by adopting HMAX model. Then, concepts are extracted from image categories and taxonomic relationship between them are created to build the ontology, which represents the semantic information associated with the training images. Secondly, two ways of training feature classifiers are applied, the first one consists in training hypernym classifiers using training images of super-categories that included images of their sub-classes. The second way, is performed by training hyponym classifiers using only images of sub-categories. Finally, test images are classified using both hypernym and hyponym classifiers, then when taxonomic relationship between the best hypernym class and the best hyponym class (that appear in top-k hyponym classes) are detected, output classifiers are merged in order to assign best classes to test images. In this work, we have conducted an experimental study where we are focused on the improvement given by our approach. It is worth to be noted that our methods, using HMAX and BoVW models with ontology, achieve superior performance to the baseline methods. Also, using the ontology, the difference in classification performance between HMAX method and BoVW method is much smaller. Moreover, we compared our methods with the Inception-v3 model. Our methods outperform the Inception-v3 on some sub-classes and accuracy values are almost the same for some super-classes. For future work, the idea would be to evaluate our approach on a large image dataset, as well as to exploit other semantic relationships between classes.