Cedric Letort
email: cedric.letort@enst-bretagne.fr

Bruno Vinouze

Bruno Fracasso

Design and fabrication of a high-density 2D fiber array for holographic switching applications

Keywords: optical fiber array, wavelength-selective switch, spatial light modulator, liquid crystal spatial, light modulator, binary-phase hologram

Holographic two-dimensional beam steering using a pixellated spatial light modulator ͑SLM͒ is a flexible and accurate technique to implement advanced space-switching, building-block functions for optical network nodes. We present the design of a specific fiber array for largecapacity holographic switches. We use two-dimensional and irregular array topology to optimize the space-bandwidth product of the SLM and to avoid crosstalk due to the coupling of higher diffraction orders. The fiber-array module is integrated by inserting the fibres through a pair of etched silicon plates aligned and fixed using a steel mount. The device is tested through near-field and fiber-to-fiber free space coupling tests. An average fiber-to-fiber insertion loss of 1 dB is achieved for a prototype containing 64 fibers.

Introduction

In the last few years, dense wavelength-division multiplexed ͑DWDM͒-based optical telecommunications networks have been massively deployed to face the dramatic traffic rise generated by Internet services. The introduction of transparent cross-connecting nodes in the optical network is increasingly put forward to obtain more flexibility and reconfigurability at the optical layer. [START_REF] Strand | Realizing the advantages of optical reconfigurability and restoration with integrated optical cross-connects[END_REF] A key issue then is the availability of flexible all-optical switching modules, in which different operations are implemented, such as space routing, wavelength extraction, or power equalization.

In this context, the wavelength-selective switch ͑WSS͒ appears as a key technology that performs both selective routing and dynamic power control of the optical channels. [START_REF] Bonenfant | OFC 2003 workshop on wavelength selective switching based optical networks[END_REF] Although planar ligthwave architectures [START_REF] Goh | Four-degree hub switch module using multi-chip planar lightwave circuit integration technology for transparent roadm ring interconnection[END_REF] yield WSS implementations with high density integration, free space-based solutions are currently the most promising in terms of insertion loss ͑Ϸ5 dB͒, channel isolation ͑40 dB͒, and polarization sensitivity ͑PDL Յ1 dB͒. Technologically speaking, the dynamic spatial-routing function of the optical channels is performed through beam deflection using either microelectromechanical systems ͑MEMS͒ mirror arrays [START_REF] Marom | Wavelength-selective 1 ϫ k switches using free-space optics and mems micromirrors: Theory, design, and implementation[END_REF] or a two-dimensional liquid crystal spatial light modulator ͑LC-SLM͒. [START_REF] Baxter | Highly programmable wavelength selective switch based on liquid crystal on silicon switching elements[END_REF] Within that frame and before the advent of WSS, holographic beam steering using LC-SLMs had been demonstrated as an interesting technique to increase the accuracy, repeatability, and stability of micro-beam steerers within optical space switches. [START_REF] Crossland | Holographic optical switching: The 'roses' demonstrator[END_REF][START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] Optical space switching was also backed up with dynamic wavelength-filtering capabilities. This was demonstrated for a one-dimensional 1 ϫ 14 holographic switch exhibiting 8.5-dB loss with multicast capabilities. [START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] The major limitation was a rather poor channel isolation level ͑-21 dB max͒, due to a one-dimensional beam steering and fiber array geometry. Moving to a twodimensional geometry was then considered a decisive option to increase the switch capacity while decreasing the cross-talk levels. The first step has already been performed with the specific adaptation of a two-dimensional LC-SLM for telecommunications uses on a pre-industrial levels. [START_REF] Heggarty | Silicon backplane ferroelectric liquid crystal spatial light modulator for uses within an optical telecommunications environment[END_REF] The device was a silicon backplane 1280ϫ 1024 pixel ͑SXGA͒ ferroelectric spatial light modulator operating in the 1.55-m wavelength range. The second step consists of designing and fabricating a 2D fiber-array structure providing high-capacity and low-crosstalk values in the present holographic beam-steering configuration. This will be presented throughout this paper, which is organized as follows:

The diffraction model for holographic beam steering is presented in Section 2 to describe the diffraction orders distribution impinging onto the output plane ͑replay field͒. This determines the constraints for the fiber-array design algorithm. Section 3 presents the design and fabrication of a specific 2D fiber-array prototype meeting the capacity and crosstalk requirements. The component is then tested and the experimental results are presented in Section 4.

Fiber-Array Custom Design 2.1 Holographic Switching Setup

The principle of a 1 ϫ N holographic optical space switch [START_REF] Crossland | Holographic optical switching: The 'roses' demonstrator[END_REF][START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] is depicted in Fig. 1. The input fiber mode is imaged onto the targeted output fiber core using a folded telecentric optical setup ͑4f architecture͒. The positive lens is an achromat doublet coated with a 1.5-m antireflection layer, whose focal length and numerical aperture are determined to minimize aberration and vignetting of the expanded fiber-mode field. The intermediate beam deflection is performed by a reflective LC-SLM displaying phase gratings ͑or holograms͒, which, since the input and output fibers are located in the same fiber array holder, permits us to limit the switch length while improving the opto-mechanical alignment procedure. The angular deviation resulting from the beam passing through the hologram is transformed into a spatial offset at the output ͑replay͒ plane. Overall, this system can be considered a fiber spatial correlator. Compared to MEMS reflective techniques, diffractive beamsteering provides motionless and high-capacity space switching with wavelength-band selection capabilities. [START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] Both techniques have their own advantages: Micro-mirror arrays have low loss and are intrinsically independent of the polarization and wavelength of the input light. On the other hand, LC holographic modules operate with no moving parts and can be used not only to steer optical beams but also to address several output fibers simultaneously, providing natural channel-bridging capabilities, as well as to shift the spectral response of the selected optical output. [START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] These two points represent a decisive advantage over MEMS technology in terms of system flexibility.

Using liquid crystal technology, various phase modulations can be achieved depending on the LC material choice. An almost continuous-phase profile can be obtained with a nematic LC, but the phase modulation depth is polarization-dependent. In contrast, a Smectic C liquid crystal-often referred to as ferroelectric liquid crystal ͑FLC͒-makes the device purely polarization-insensitive, with a switching time of a few hundreds of microseconds, [START_REF] Crossland | Holographic optical switching: The 'roses' demonstrator[END_REF][START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF][START_REF] Heggarty | Silicon backplane ferroelectric liquid crystal spatial light modulator for uses within an optical telecommunications environment[END_REF] but to the detriment of diffraction efficiency. The recent introduction of 2D-LCOS ͑Liquid Crystal on Silicon͒ SLM devices operating in the 1.55-m telecommunication window for both WSSs 5 and holographic switches 8 makes the design of large-capacity ͑i.e., output port count Ͼ100͒ devices possible.

In that framework, a key step is the design of an optimized 2D single-mode fiber array that takes advantage of the high-output port count addressing capabilities of holographic deflectors.

Holographic Beam-Steering Model

A detailed analysis of the optical reconstruction generated by pixellated and phase-quantized LC-SLMSs can be found in the literature. [START_REF] Tan | Dynamic holography for optical interconnections. II Routing holograms with predictable location and intensity of each diffraction order[END_REF] We shall briefly present the reconstruction model for binary and pixellated hologram patterns with the purpose of determining the relevant steering parameters, Fig. 2 Three-dimensional higher diffraction order distribution generated by a binary-phase 2D grating. namely the intensity and location of the higher diffraction orders. Using this information, we will be able to derive the beam-coupling ratio at any position of the output fiber plane and hence design a 2D fiber-array topology that minimizes the holographic switch crosstalk. Geometrical constraints like aberration and vignetting at the Fourier lens will have a limited influence if the optical setup is properly designed. [START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] These parameters will hence not be considered in the model. The ideal routing holographic pattern is the blazed grating hologram that produces a unique replay of the input fiber mode at a given output fiber location. More precisely, if ͑a j , b j ͒ are the coordinates of the jth output fiber ͑i.e., the core center location͒, the two-dimensional information to be displayed at the SLM plane is the periodic phase-only distribution given by H͑x,y͒ = exp ͭ -2i

f ͑a j x + b j y͒ ͮ ,

͑1͒

where and f denote, respectively, the wavelength and the focal length. The spatial periods in the x and y dimensions are given by T x j = f / a j and T y i = f / b j . The encoding of distribution ͑1͒ onto a pixellated binary-phase function is required to physically display the steering information onto an FLC-SLM. This operation can be represented by a procedure involving three steps. The phase of the steering function H is first quantized by comparing the real part R͑H͒ of the complex function to a threshold level . The resulting binary phase grating H q takes the phase level if R͑H͒ Ͻ and the + value otherwise. The value controls the compression ratio ␣ of the binary grating H q . The optimized case is = 0 as it yields a balanced grating with ␣ = 0.5, and hence no energy in the zeroth diffraction order. Then, the pixellation and dead-space ͑interpixel͒ effects are introduced. If we consider a pixel pitch value of p and a pixel size of p 0 ͑p 0 Ͻ p is the size of the modulating pixel zone͒, the holographic steering distribution H s ͑x , y͒ will result from a multiplication of H q with a 2 Dirac distribution W p ͑x , y͒ ͑p is the pixel grid/pitch͒, followed by the convolution of the result with a square distribution ͟ p 0 of size p 0 ϫ p 0 , representing the pixel shape. The SLM fill factor is given by the ratio F = ͑p 0 / p͒ 2 . This finally gives us

H s ͑x,y͒ = ͕H q ͑x,y͒W p ͑x,y͖͒ * ͟ p 0 ͑x,y͒, ͑2͒
where ͟ p 0 ͑x , y͒ =1 if ͉x ͉ Յ p 0 and ͉y ͉ Յ p 0 , and ͟ p 0 ͑x , y͒ = 0 elsewhere, * standing for the convolution operation.

The SLM limited size will be discussed later. Note from relation ͑2͒ that the grating periods T x and T y are now quantized by the pixel pitch p in the form T x = pN x and T y = pN y , with N x and N y the number of pixels per period in each dimension. The relative complex amplitudes C lmn and positions P lmn of all the replay orders can be determined by calculating the Fourier transform H ˜s͑ , ͒ of H s ͑x , y͒ from ͑2͒, yielding, 10 for l 0,

C lmn = sinc͑␣l͒sinc ͭ ͱ F ͫ m + 1 N x ͬͮ sinc ͭ ͱ F ͫ n + 1 N y ͬͮ and P lmn = ͩͫm+ 1 N x ͬ , ͫ n + 1 N y ͬͪ ,

͑3͒

where sinc͑x͒ = sin͑x͒ / ͑x͒, =2␣F, and = f / p. As illustrated in Fig. 2, relation ͑3͒ represents a threedimensional diffraction-order ͑l͒ distribution, where the replication is caused by the phase-level binarization, while the spatial quantization operation ͑pixellation͒ produces the 2-D replication along with the m and n directions. Due to the spatial convolution generated by the 4f optical setup, the input fiber mode will be spatially convolved with the H ˜s͑ , ͒ distribution, yielding a constellation of weighted and shifted replications as given by relation ͑3͒. The ͑l =1,m =0,n =0͒ term is the main "peak" addressing the output fiber at position ͑a , b͒. We can then derive from ͑3͒ the diffraction efficiency , as a function of the grating periods

͑N x , N y ͒: ͑N x ,N y ͒ = ͉C 100 ͉ 2 = 4F 2 2 sinc 2 ͩ ͱ F N x ͪsi nc 2 ͩ ͱ F N y ͪ, ͑4͒
where we have considered a balanced grating ͑␣ = 0.5͒ for simplicity. The upper limit in is obtained when N x , N y Fig. 3 Diffraction efficiency evolution versus pixel per period number N x as a function of N y . Negative values correspond to symmetric gratings with respect to the y-axis.

Fig. 4 Exclusion disk method to remove potential crosstalk positions from the fiber location search space.

→ ϱ and F → 1, yielding max =4/ 2 = 0.41. Figure 3 shows that diffraction efficiency ͑or steering efficiency͒ is relatively constant for grating period values N x Ն 4 and decrease significantly for smaller pixel per period number. Expression ͑4͒ being symmetric with respect to N y , we shall restrict the grating period search space to N x Ն 4 and N y Ն 4, which will ensure that ͑N x , N y ͒ Ն 0.3.

The steering model could be further refined by considering deviations in the FLC cell parameters with respect to their optimal value, e.g., cell thickness, birefringence, and tilt angle. However, such variations will not affect the crosstalk evaluation and hence the basic steering model will be kept for the remainder of the discussion. Conversely, the limited size of the SLM will modify the output plane irradiance distribution, and the correction will be included in the fiber-array design algorithm presented in the next sec-tion. The final step of the modeling will consist of calculating the coupling ratios between the input replicated modes and the output fibers. This point will be addressed in the next section.

Crosstalk Model

The input fiber mode replica as given by ͑3͒ will potentially generate crosstalk at the output plane. The description of the crosstalk events strongly depends on the output fiber placement process, which will be detailed in the next section. For the moment, we suppose that N f fibers have been placed, each corresponding to a position ͑a j , b j ͒ with 1 Յ j Յ N f , with an addressing grating described by an ͑N x j , N y j ͒ pair. The question then is to know if a new grating with ͑N x j+1 , N y j+1 ͒ pixels per period is acceptable taking into account the fiber positions already set. Using its main ͑1,0,0͒th order, this grating will address a fiber position located at the output coordinates a j+1 = / pN x j+1 and b j+1 = / N y j+1 . Hence, two types of crosstalk conditions can be considered. First, the crosstalk between the higher orders corresponding to previously placed fibers and the virtually present tested fiber ͑referred to as past or P-crosstalk͒, and, second, the crosstalk between higher orders of the present ͑N x j+1 , N y j+1 ͒ grating and the set of previously placed N f fibers ͑referred to as future or F-crosstalk͒.

Crosstalk evaluation is based on the calculation of the optical intensity coupling of the incident Gaussian beams in the closest output fibers. We shall suppose that the optical setup is correctly aligned, with no focusing offset and tilt angle between the incident and output modes. The two waists ͑incoming beam and output fiber͒ will also be supposed to be equal, as the fibers are located in the same array. Under these assumptions, crosstalk evaluation will depend only on the distance ⌬r between a given incident input mode replica position P lmn k ͓corresponding to a previously calculated k grating, as given by ͑3͔͒ and the present tested output fiber position ͑a j+1 , b j+1 ͒. The intensity coupling ratio between the incident mode and the fiber will be given by

I coupl ͑⌬r͒ = ͉C nlm k ͉ 2 exp ͩ -⌬r 2 0 2 ͪ ,

͑5͒

where 0 is the waist radius of both the incident Gaussian beam and the output fiber. The resulting power isolation value ͑normalized with respect to the diffraction efficiency͒ will be given by the relation

X͑dB͒ = 10 log 10ͩ I coupl ͉C 100 k ͉ ͪ Ϸ -4.3ͩ ⌬r 0 ͪ 2 + 20 log 10ͩ ͉C nlm k ͉ ͉c 100 k ͉ ͪ .

͑6͒

In order to process the crosstalk issue in an efficient manner in the fiber-positioning algorithm, one approach is to define forbidden crosstalk areas surrounding higher-orders positions so that future fibers cannot be placed if the port isolation is worse than a given reference value X 0 .

As an example, we consider the higher diffraction order shown in Fig. 4 and located at position P lmn k . Using Eq. ͑6͒, a radius ⌬r͑X 0 ͒ is calculated so that an isolation value better than X 0 is obtained from ͑6͒ if the output fiber is placed outside a disk of center P lmn k and radius ⌬r͑X 0 ͒. An additional safety radius margin corresponding to a fiber diameter d is added to compensate for possible fiber placement errors occurring during the fabrication process. This finally means that no optical fiber core center will be allowed in a disk of center P lmn k and radius R = ⌬r͑X 0 ͒ + d.

Fibre Array Design Algorithm Principle

Considering the beam-steering model described so far, the next step will consist of designing a fiber-array topology meeting the two major constraints pursued for the holographic switch i.e., ͑1͒ high-capacity and ͑2͒ low crosstalk levels. We propose an iterative procedure depicted in Fig. 5, for which the basic idea consists of sweeping the grating period space. For each ͑N x , N y ͒ value, the placement of a fiber at the addressed position ͑i.e., ͓1, 0, 0͔th, deflected output peak location͒ will be validated or not, depending on the following three conditions:

1. Diffraction efficiency constraint: The first step of the fiber placement process is to select an ͑N x , N y ͒ pair greater than a specified value ͑N x , y Ͼ 4͒ corresponding to high steering efficiency in the first diffracted order ͑see Fig. 3͒.

Minimal distance between fibers:

The fiber placement density is limited by the minimum spacing ␦ between two adjacent fibers. Considering a standard SMF fibers with a 125-m cladding diameter, we will choose ␦ = 130 m. Using thinned cladding optical fibers ͑down to 40 m͒ is technically feasible but will not be considered here.

Crosstalk condition:

The position and intensity of all the diffraction orders in the output plane are calculated using ͑3͒. The power isolation of each k input mode replica in the present ͑N x , N y ͒ output fiber is calculated and compared to a reference threshold ͑X 0 = 40 dB is the standard value͒. Equation ͑6͒ of the crosstalk forbidden zone is derived and transformed into an equation in N x and N y to determine the grating period that will be rejected.

At last, if all criteria are respected, the new position corresponding to the ͑N x , N y ͒ pair is accepted. If not, the position is rejected.

Capacity estimation

The fiber-addressing capacity intuitively depends on both the fiber core diameter d and the size of the addressed space in the output plane per dimension, given by ⍀ = f / p ͑p is the pixel pitch͒. The inversely proportional relationship between the number of pixels per period N x and the output position a reached by the first order ͑a = / N x ͒ generates densely populated areas at the center of the addressing zone ͑for large N x values͒ and sparsely populated areas away from the center ͑smaller N x values͒. Thus, an estimate N of the holographic switch capacity per dimension can be given [START_REF] Letort | Dynamic spectral response of a liquid crystal based optical channel selector[END_REF] by

N = 2ͩͱ ⍀ d -1ͪ . ͑7͒
For the standard parameter values as used in Ref.

Results

The algorithm shown in Fig. 5 is implemented using a computer-controlled procedure. The algorithm parameter values are intrinsically linked to such experimental conditions as SLM physical properties, optical setup dimensions, and fiber array technology. Thus, the fiber placement process will generate a non-unique solution, depicted in Fig. 6.

The base pattern ͑A-zone͒ contains 32 fibers configured on an irregular lattice. It is replicated by a vertical axial symmetry to form the D-zone. Point symmetries with respect to the central fiber ͑denoted by "E"͒ provide the C-and B-zone, respectively. Nine other fibers are placed along the x-and y-axes of the output plane. This finally gives a total capacity of 141 fibers located in a 1.2-mmϫ 1.2-mm square, this topology meets the previously defined crosstalk requirements. This configuration permits us to exploit the symmetrical position of diffraction orders generated by binary phase gratings. This property can be useful to implement a 1 + 1 bridging function in the switching device, [START_REF] Fracasso | Design and performance of a versatile holographic liquidcrystal wavelength-selective optical switch[END_REF] providing natural optical protection of the WDM network layer.

The layout shown in Fig. 6 will be used as a reference pattern to fabricate 2D matrices, from low capacities ͑Ͻ10 ports͒ to several tens of output fibers.

3 Fiber-Array Fabrication

2D Fiber Holder Etching

The first step in the fiber-array manufacturing process is the choice of a 2D fiber holding technology. In this context, a well-known technology consists of superimposing 1D V-grooves, [START_REF] Corning | [END_REF] which enables the fabrication of fiber arrays in various materials with a high positioning accuracy. As an alternative solution, a fiber-array fabrication process in which the fibers are embedded in adjacent ceramic ferrules arranged in a honeycomb topology has been proposed. [START_REF] Chanclou | Highly efficient collective coupling between laser diode arrays[END_REF] The device was equipped with individual micro-optics at the end face of the fibers in order to enlarge the mode diameter and thus release the alignment constraints related to optical free space interconnections. A third technology option is based on micro-holes etching through a silicon plate. This mechanical holding system is used to minimize the fiber location and pointing errors, provided that the hole depth is long enough to guide and maintain the fiber in the reference position. A reasonable value for the drilled holder thickness is four times the fiber cladding diameter, i.e., about 500 m for standard SMF fibers. This method also provides high flexibility and accuracy in the fiber placement scheme. Due to the fiber density required ͑as shown in Fig. 6͒, this is the preferred option here. The basis for our fiber-array integration process is a silicon plate holder ͑530-m thickness͒, whose thermal expansion coefficient is known to be very low, containing precisely etched holes placed as in Fig. 6. This micro-drilled plate is fabricated using a complex deep reactive ion etching ͑DRIE͒ process, providing holes with conical shapes on one side for an easier fiber insertion. The measured entrance and exit hole diameter specifications are 130Ϯ 2.0 m and 126Ϯ 1.0 m, respectively. Although controlling the etched holes profiles with such an accuracy is a complex task, several batches of components were successfully etched and cleaned ͓Fig. 7͑a͔͒. The fibers are then precisely inserted in the desired etched holes using a threading process controlled with a binocular microscope. The fibers are then fixed to the silicon plate with an Epotecny NOA 61 UV glue dedicated to the integration of opto-mechanical systems. This operation is achieved in a vacuum chamber in order to fill the gap between the fiber claddings and the hole walls. Each fiber is then reinforced with a polymer sheath whose diameter d s depends on the final fiber-array capacity ͑typically, d s = 900 m for capacity Յ32 fibers and d s = 600 m for capacity Ն32 fibers͒.

Four types of fiber arrays with different capacities of 16, 32, 64, or 128 fibers are manufactured using the fiber holder shown in Fig. 7͑a͒.

Fiber-Matrix Packaging

In a second step, a steel mount is specifically machined to the silicon plate geometry, which is precisely fixed to the output face of the steel mount using Epotecny UVS-91 UV glue. This operation will determine the final ouput fiberplane direction. As illustrated in Fig. 8͑a͒ the optical fibers are then collected with a heat-retractable sheath that is interlocked with the steel mount using liquid NOA 61 UV glue, so that the optical fiber,array remains mechanically reinforced during the next critical polishing step.

The polishing phase is performed with a custom procedure to maintain flatness and optical quality across the array. The fiber-to-fiber coupling ratio will directly depend upon the quality of the polishing step. The difficulty here is to impose the right fiber end-face orientations ͑i.e., right angle with respect to the fiber axes͒ by polishing all the fibers simultaneously and in the same conditions. To this aim, a custom-designed stainless steel polishing clamp is used to fix the fiber-array mount during the whole polishing operation. The fiber array is polished using a horizontal rotating platen with an abrasive surface whose granularity is gradually reduced from 50 m to 0.3 m. The material residue draining process is optimized in order to avoid weakening of the output fiber surface. This point is periodically controlled during the process by observing the output surface through a binocular microscope. Figure 8͑b͒ shows a typical image of an acceptable end-face quality for the fiber array after the polishing stage ͑64 fibers have been threaded͒.

Experimental Test Results

The purpose of the fiber-array characterization procedure is twofold. First, we will evaluate the optical quality of the fiber-array end face in order to validate the device fabrication process; second, the fiber matrix is tested in an optical fiber-to-fiber interconnection setup to evaluate the fiber-tofiber insertion loss.

Output Fiber-Plane Test

Near-field tests

The near-field experimental setup is used to measure the output fiber-beam shape. An infrared camera * is placed on the fiber-array optical axis, and optical beam profile measurement software is used to display the optical beam profile in both the vertical and horizontal axes. Figure 9 shows the measured intensity profile in two figure orthogonal directions, featuring the ideal case of a single-mode Gaussian beam emerging from a fiber end face with good optical quality.

Output beam angular tests

The same experimental setup principle is used to test the output fiber-surface orientation. The infrared camera is placed here at two different positions of the optical axis in order to compare the positions of the Gaussian beam center between a reference fiber and the tested fiber ͑cf. Fig. 10͒.

The angular deviation ␦ of the beam emerging from the tested fiber is determined by the equation

tan͑␦͒ = ͑d 2 -d 1 ͒ l , ͑8͒
where d 1 and d 2 are the two distances between the incident Gaussian beam centers from the two fibers measured at two positions separated by a distance l. The angular deviation can then be used to estimate the coupling efficiency variation between the input and ouput fibers ͑the latter is supposed to be perfectly aligned on-axis͒:

I coupled ͑␦͒ = exp ͫ -ͩ 0 ͪ 2 • ␦ 2 ͬ .

͑9͒

The angular tests performed on a statistical set of 20 fibers have shown a negligible angular deviation, as they produce an average coupling efficiency variation lower than 0.1 dB.

Fiber-to-Fiber Coupling Loss Measurement

An important step to assess the performance of the fiber matrix is to test the fiber-to-fiber coupling loss. This is achieved by replacing the SLM of Fig. 1 with a mirror optimized for near-infrared operation. The lens used is an achromat doublet ͑Melles-Griot͒ composed of two different glasses ͑BaLKN3 and SF15 † ͒. The fiber array is placed on 6-axis micro-positioning system in the reflective 4f setup shown in Fig. 11. The optical micro-bench is first aligned using a visible He-Ne laser light that is injected in the input fiber and coupled to an output fiber by moving the fiber array. The fiber-to-fiber coupling loss is then measured using infrared light at 1.55-m wavelength. The operation is repeated for a large number of input/output fiber pairs. Test results are proposed in Figs. 12 and 13 for 32 and 64 output fiber arrays, respectively, with a collection of more than 100 measurements between randomly chosen fiber pairs. The mean measured fiber-to-fiber losses are, respectively, 1.1 dB and 0.9 dB, with 0.3-dB standard deviation in both cases. These values are encouraging as the fiber end faces are not antireflection-coated.

The final test consists of measuring the polarizationdependent coupling loss variation. It is performed on a set of randomly chosen fibers ͑the fiber array used is the component containing 64 fibers͒ by inserting a polarization controller between the emitted fiber and the addressed fiber. The fiber-to-fiber coupling efficiency variations between the maximum and minimum values for different polarization states are given in Table 1 and reveal a negligible polarization-dependent loss for the tested fiber array.

Conclusions

We have reported the design of a 2D irregular fiber array dedicated to fiber-to-fiber holographic switching using a binary-phase steering device. We have proposed a specific high-density output fiber placement algorithm based on coupling efficiency maximization and crosstalk minimization.

Fiber-array prototype fabrication steps have been described, and two devices have been tested. The best component exhibits a 64-output fiber capacity with less than 1 dB fiber-to-fiber insertion loss and negligible polarization-dependent loss.

The next step is to integrate the fabricated fiber array in a dynamic holographical fiber-to-fiber switching setup. This operation as well as the performance of the resulting device are presented in a submitted publication. [START_REF] Letort | Design and test of a 1 ϫ 64 and crosstalk-free 2D-holographic liquid-crystal optical switch[END_REF]

Fig. 1

 1 Fig. 1 Reflective holographic space-switch architecture.

Fig. 5 Fig. 6

 56 Fig. 5 Fiber core placement algorithm principle.

Fig. 7 ͑a͒

 7 Fig. 7 ͑a͒ Top view of the bare silicon fiber holder with etched holes. ͑b͒ Side view after the fiber threading step.

Fig. 8 ͑a͒

 8 Fig. 8 ͑a͒ Fiber-array packaging view. ͑b͒ Exit side of the fiber array after the polishing stage ͑64 fibers have been threaded͒.

  7 ͑p =13 m, f = 40.5 mm, d = 125 m, and = 1.55 m͒, one obtains a 2D capacity estimate of N 2 = 110 fibers. It is expected, however, that this value will be exceeded by the results obtained with the truly 2D geometrical search performed by the iterative algorithm described above.

Fig. 9 Fig. 10

 910 Fig. 9 Result of polished output fiber beam shape with Gaussian profile measured with near-field experimental setup.

Fig. 11

 11 Fig. 11 Fiber-to-fiber coupling loss measurement setup.

Fig. 12

 12 Fig.12 Fiber-to-fiber coupling loss results for a 32-output fiber array.

*

  Type IR-Vidicon camera; Model C2400-03; sensitive surface 12.9͑H͒-9.5͑V͒; horizontal lines: 575 ͑CCIR͒. † Model #01LAL011.

Fig. 13

 13 Fig.13 Fiber-to-fiber coupling loss results for a 64-output fiber array.

Acknowledgments

The authors would like to thank the Optogone/Memscap and PHS MEMS companies for funding and performing the wafer-etching process, and Michel Gadonna ͑ENST Bretagne͒ for valuable discussions concerning the nearfield characterization setup.

Bruno Fracasso is an associate professor at GET/Ecole Nationale Superieure des Telecommunications de Bretagne ͑Brest, France͒. He received an engineering degree from Ecole Nationale Supérieure des Telecommunications de Bretagne in 1989 and a PhD degree in Electrical Engineering from Université de Haute Alsave, Mulhouse ͑France͒ in 1993. He is currently involved in research projects on optical switching for and routing within fiber networks, combining device modelling, simulation and technological integration.


Adrian Popescu

Pierre-Alain Moellic

Ioannis Kanellos

Pierre-Alain Moëllic

THEMEXPLORER: FINDING AND BROWSING GEO-REFERENCED IMAGES

come

INTRODUCTION

Using metadata associated with digital images is known to improve the way users interact with large scale photographic collections [START_REF] Naaman | Automatic Organization for Digital Photographs with Geographic Coordinates[END_REF]. In particular, geographic information associated to pictures enables the location of the data on a map and propose an imaged description of the world. New online applications propose the visualization of geographic entities using geo-referenced photos. Most noticeably, Flickr proposes a map-based navigation 1 of their geo-referenced data and Google Earth mixes Panoramio [18] images. In both interfaces, the user can navigate on the maps and click spots that correspond to a place where pictures were taken. In [START_REF] Ahern | World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections[END_REF] tags representing a region are displayed and, once a tag is selected, corresponding pictures are displayed as a result. This type of interface is more informative and improves navigation over Flickr and Google in which the interaction with the map information is usually limited to the presentation of pictures (or tags) from the region. ThemExplorer proposes further enriched user interaction without any supplementary burden on the user. As in [START_REF] Ahern | World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections[END_REF], we employ TagMaps [26], the tag visualization Webservice proposed by Yahoo! as the underlying service to present geographic names to the users. In our application, the geographical database is partially adapted from Geonames, a freely-available geographical database and partially extracted from heterogeneous information sources on the Web. The enrichment of the 1 http://flickr.com/map geographical database is particularly important for regions that are not well represented in Geonames. The obtained structure enables a topical browsing of geo-referenced pictures that is not includedto our knowledge -in any other geographic image visualization tool. The pictures associated to locations are themselves collected using Flickr and Google Image. We include a content-based image retrieval (CBIR) functionality in ThemExplorer. The CBIR limited to photos representing the same geographic name in the database and is especially useful for popular tags having a lot of associated pictures, allowing a quick in-depth exploration of the answers set.

RELATED WORK

Flickr and Google Earth propose map-based visualization tools that place pictures on a map using the geographical coordinates of the geo-referenced pictures. The textual information guiding the search is limited to the one on the map and the user interaction is reduced to clicking individual photos in order to have a detailed view. Panoramio [18] presents image snippets on a map and a set of images near the map. An important particularity of the platform is that any submitted picture has to be validated as representative by a different user, increasing the chances for the images to be relevant. The system that is the closest to ours is World Explorer, described in [START_REF] Ahern | World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections[END_REF] and concerns the visualization of tags on a map and of corresponding pictures in the interface. The authors introduce a generative method for identifying locations based on unstructured data from Flickr. A multi-scale analysis is performed in order to find the most representative tags at each level on the map. One advantage of the approach is that the number of presented tags is not limited a priori and, with the growth of the unstructured textual database, it is possible to mine and prompt more and more information. An evaluation of the dataset used in [START_REF] Ahern | World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections[END_REF], included in [START_REF] Rattenbury | Towards Automatic Extraction of Event and Place Semantics from Flickr Tags[END_REF], shows that the identification of locations is correct in 82% of the cases (when half of the candidates are retained). The use of a dictionary constitutes a simple way to eliminate irrelevant tags for the geographic domain, but this method is not employed in [START_REF] Ahern | World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections[END_REF]. The main difference between World Explorer and ThemExplorer is that, in our system, the database is structured. Consequently the navigation model and the interaction process are different in the two applications. The development of large-scale geographical thesauri received considerable attention in the Geographical Information Systems community. The Alexandria gazetteer [START_REF] Hill | Geographic namesthe implementation of a gazetteer in a geo-referenced digital library[END_REF] contains more than 4 millions entries, grouped into several general categories (like landforms, manmade, hydrographical forms etc.), that are further divided into finer levels (like mountains, buildings or streams). Geonames [7] is another geographical thesaurus, containing more than 6 millions entries. One of the main problems with existing databases is that their coverage is highly variable. For example, in Geonames, the USA are represented by over 1.8 million instances, while Serbia and Montenegro have only 6 associated entries. A possible way to overcome this variability is to try to automatically enrich the database. Recently, Auer el al. [START_REF] Auer | DBpedia: A Nucleus for a Web of Open Data[END_REF] introduced DBPedia, an interesting encoding of Wikipedia pages in database format. This structure contains over 70,000 geo-referenced entries, which can be used for mining detailed information about geographical entities. The processing of geographic information in this database is minimal since it does not classify the geographic names into higher-level classes. The inclusion of geographical thesauri in image visualization frameworks is mentioned in [START_REF] Naaman | Assigning Textual Names to Sets of Geographic Coordinates[END_REF] but it is left for future work. One important limitation over automatically-built database concerns their coverage, which determines the extent of information it is possible to display, whereas the volume of information extracted using a generative method like the one employed in [START_REF] Rattenbury | Towards Automatic Extraction of Event and Place Semantics from Flickr Tags[END_REF] depends only on the constantly growing volume of data in Flickr. A lot of research effort in the image retrieval community was directed toward the development of CBIR techniques. In spite of an impressive number of publications addressing the problem (see [START_REF] Veltkamp | Survey of Content-Based Image Retrieval Systems[END_REF], [START_REF] Liu | A survey of content-based image retrieval with high-level semantics[END_REF]), these techniques are not exploited in large scale applications, like Web image search engines. Cortina [START_REF] Quack | Cortina: A System for Largescale, Content-based Web Image Retrieval[END_REF], with 11 millions processed images, is the largest existing CBIR application. The reticence in including query-by-example in Web image search engines can be explained by the semantic gap [START_REF] Liu | A survey of content-based image retrieval with high-level semantics[END_REF] that emerges from a human's point of view when we have to deal with poor results, especially for large datasets [START_REF] Popescu | Ontology Driven Content Image Retrieval[END_REF]. Equally important, content processing introduces an important burden in image retrieval applications when targeting large volumes of data. O'Hare et al. [START_REF] O'hare | Combination of content analysis and context features for digital photograph retrieval[END_REF] assess the use of CBIR techniques for georeferenced pictures. Several image descriptors are first employed alone, then combined with a restriction of the search space based on spatial proximity as well. This last setting performs best, but the results are not drastically improved compared to classical CBIR. Note that no textual information attached to pictures is exploited in [START_REF] O'hare | Combination of content analysis and context features for digital photograph retrieval[END_REF]. Liu [START_REF] Liu | A survey of content-based image retrieval with high-level semantics[END_REF] reviews over 100 research papers approaching different ways to improve CBIR by adding semantic information, but this topic is still a hot one. An effective way to improve visual retrieval is to introduce it as a complement of text-based image retrieval and limit the search space based on prior textual information introduced by the user like in [START_REF] Smith | Visually searching the Web for Content[END_REF] or [START_REF] Popescu | Ontology Driven Content Image Retrieval[END_REF]. Indeed, in [START_REF] Popescu | Ontology Driven Content Image Retrieval[END_REF], a multi-level limitation of the search space is proposed and the most specific level ensures both visual and conceptual coherence. In this paper, we apply the restriction of the search space to specific geographic entities like Mont Blanc or Eiffel Tower. [START_REF] Sanderson | Search words and geography[END_REF] discusses the result of a log file study, showing that geographic query represent a hefty chunk of the users of information search engines. Moreover, the geographical domain seems to include the largest share of repeated queries.

GLOBAL SYSTEM DESCRIPTION

System Architecture

In figure 1 we give a general view of the architecture of ThemExplorer. A video demo of the system is available 2 .

2 http://www.vimeo.com/441282 When the user asks for geographic names in a particular region, TagMaps is called and queries the geographical database so as to obtain the tags. When the user selects a displayed name, the system checks if this is a first selection; if so, the associated pictures are immediately presented; otherwise, the photos are searched on the Internet, firstly by using Flickr and, secondly, if the number of the collected images is not enough, by using Google Image. When the user launches the CBIR facility, a content-based search engine (called PIRIA [START_REF] Joint | A general tool for indexing, search and retrieval of multimedia content[END_REF]) is employed. The locally stored pictures are visually indexed and the closest answers to the image query are computed and displayed.

The user interface

The user interface (Figure 2) aggregates the different elements of ThemExplorer. It is implemented in PHP for dynamically generating the content of the page and Javascript for the TagMaps linking. The interface is made up of three main components:

• a map obtained from TagMaps displaying the most representative geographic names in a region • a series of geographic categories (from Geonames) enabling a selection of one or more topics of interest • an image display area where the user can launch the contentbased facility

Interaction Process

The interaction is determined by two types of restriction: a thematic restriction obtained using conceptual inheritance and a spatial restriction, provided by the multi-scale map visualization tool.

The selection of one or more themes of interest is performed by browsing a categorical tree containing two levels of detail. At the most general level, the user can select one general "geographic" category, like Artifacts or Nature. A finer grained selection is proposed at the second level: the user can choose one or more subclasses like Bridge, Church or Museum. The map enables the selection of a region of interest, either by typing a location name or by using a zooming tool. The items displayed on the map can be selected to make appear associated photos. If the user picks a particular image, this picture becomes an example query and a content-based retrieval among the photos associated to the same tag is launched. We constrain the content based search because the results of this type of query are poor for large and heterogeneous image collections like those in Flickr and Google Image.

GEOGRAPHIC ENTITIES EXTRACTION

In this section we first describe the different resources we used to build a large-scale and robust geographic database. Then we explain how we enrich the available data to build our database. We add an Entity-Rank information that captures the relative importance of each tag in the database. This last dimension is fundamental in retrieval applications because it compels the system to present the most relevant entities in priority.

Formalism

To describe a geographic entity we use the following tuple defined in [START_REF] Hill | Geographic namesthe implementation of a gazetteer in a geo-referenced digital library[END_REF]: (Entity-Name, Entity-Coordinates, Entity-Type), where the Entity-Coordinates is the longitude and latitude coordinate of an entity and the Entity-Type is the category of the entity according to the ones defined in Geonames.

Data Sources

Geonames

Geonames is a freely-available geographical database with about six millions of entities. Each instance in Geonames (e.g. Mont Blanc) belongs to an intermediary geographic class (e.g. mountain) which, in its turn, is attributed to a more general category. With such a structure, a topical browsing is possible.

Wikipedia

The well known online encyclopedia contains much gazetteer-type information. There are around 100,000 geo-referenced articles in the English version of Wikipedia [7], a volume which remains significantly smaller than the 6 millions entries already present in Geonames. We can exploit the structure in new geo-referenced articles and extract the Entity-Name (in the page title), alternate variants of the entity name (in the header, and in foreign language page pointers), as well as explicit geographic Entity-Coordinates. Entity-Type can often be extracted from the first sentence of the article [START_REF] Hill | Geographic namesthe implementation of a gazetteer in a geo-referenced digital library[END_REF]. There is no guarantee, however, that all three components of our minimal tuple will be found for each item.

Panoramio

Panoramio is a website for sharing geo-referenced pictures. The validation of photo locations by other users constitutes a useful particularity of this platform. Data introduced by the users are available via an API which returns the title and localization of the images and a link towards the image itself. We can also exploit frequency information to extract an indication of the popularity or importance of the entity (see section 4.3.2).

Geographic data enrichment

Geonames includes a very large amount of data but with an inhomogeneous coverage (see Section 2 for an example) and, given the required amount of work implied by the manual enrichment of a large scale database, the automation of the process is appealing. We briefly describe a procedure for automatically enriching Geonames based on the aggregation of heterogeneous information on the Web.

Entity extraction

First, we extend the Geonames domain model which possesses over 600 intermediate geographic concepts for tagging an Entitytype. However, a preliminary study of Wikipedia articles convinced us that this vocabulary was not comprehensive enough. We thus added around 20 new concepts for the geographic domain (e.g. Borough, Neighborhood), as well as some concepts frequently having a strong spatial connotation, such Club, Team or Laboratory which are often given with geographic coordinates in Wikipedia.

We populate the database by extracting everything we can consider as a localizable entity (Entity-Name) from Wikipedia and from Panoramio. From a downloaded copy of Wikipedia, we begin by extracting all articles which contain geographic coordinates. Following [START_REF] Kazama | Exploiting wikipedia as external knowledge for named entity recognition[END_REF], we retain this article if its first sentence contains a geographic concept (after the verb 'to be'), for example, "…is a large residential neighborhood". For these names, we have the three minimal elements: the Entity-Name, the Entity-Type, and the Entity-Coordinates. We use these articles to build a second list of other candidates from Wikipedia. From these articles, we extract all new linked proper names (identified by uppercase Wikipedia links) and access their articles. We retain these names as candidates if the article's first sentence contains a geographic concept, as described above.

Entity ranking

As we mentioned, we extended the minimal definition of a geographical gazetteer [START_REF] Hill | Geographic namesthe implementation of a gazetteer in a geo-referenced digital library[END_REF] by adding an Entity-Rank. The ranking increases the usefulness of the geographic gazetteer in information retrieval. We produce a ranking by aggregating information found in Panoramio and a search engine. While the first is well adapted to the geographic domain, its coverage is significantly reduced when compared to that of a search engine, such as Alltheweb, which provides better coverage but lower accuracy. When using Panoramio for ranking items, we take all the photos around the candidate coordinates (within a distance of 30 kilometers), and search these photos using the candidate Entity-Name. We then combine the total number of retrieved Panoramio images (a classical term frequency measure) with the number of different users (a community-based relevance assessment) having uploaded those images. We can reasonably suppose that, if a geographic object was photographed by several people, it is more representative than an object photographed by one person only, and should thus be ranked higher. The Panoramio based relevance measure is generally accurate but can return the same rank for different items. To differentiate these elements, we use Alltheweb counts for the candidate geographic name.

CBIR SYSTEM

Color and texture-based global descriptors

We used the Local Edge Pattern (LEP) [START_REF] Cheng | Image classification using color, texture and regions[END_REF] a texture-based descriptor. The LEP descriptor is based on the edge map of the image computed with Sobel filtering. A threshold is applied to quantify the pixel in an "edge" class [START_REF] Ahern | World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections[END_REF] or "non-edge class" (0). Then, a binomial kernel ({1, 2, 4},{8, 256, 16},{32, 64, 128}) is applied on the binary image to label each 3x3 micro-texture pattern with a unique value in [0,512]. Two 256-bins histograms could be computed: the first one for the non-edge pixels corresponding to patterns with label in [0,255] and the second one for the edge pixels (values in [256,512]). We quantified the RGB color space in 4 classes for each channel and added a 64-bins color histogram to the two previous texturebased histograms to form the global descriptor h g . The similarity between two pictures is computed with the classical Euclidean distance.

Bag of features descriptor

The adaptation of the "bag of words", a popular method in natural language processing, to the "bag of visual words" or "bag of features" [START_REF] Csurka | Visual categorization with bags of keypoints[END_REF] for images endows the image processing community with new and powerful methods to build robust description of images. [START_REF] Lazebnik | Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories[END_REF], [START_REF] Sivic | Efficient visual search of images and videos[END_REF] proposed very interesting analysis and uses of this method for content-based image retrieval, objects or scenes classification purposes. We use a bag of features approach in CBIR [START_REF] Horster | Image Retrieval on Large-Scale Image Databases[END_REF]. This descriptor captures local properties of represented objects and complements traditional approaches to image indexing, which provide a global characterization of the content. As shown by the evaluation in section 6, the combination of the two types of descriptors improves the performances the CBIR The basic idea of the bag of features is to produce a visual vocabulary built after an unsupervised quantification of a set of patches extracted from images and described with classical features such as the SIFT descriptor [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF]. The extraction of the patches can be realized using a random or homogeneous selection of the pixels or using interest points detectors (Harris Laplace, Gaussian based detectors, …). Usually, the amount of patches is important and the clustering process can rapidly become a real issue. The K-Means algorithm (or some "enhanced" derivates) is generally applied several times with different initializations to reach or tend to reach an optimal partition. After this quantification step, we have at our disposal a codebook or vocabulary composed of "visual words" enabling to represent an image with the histogram of the occurrence of each "visual word" of this vocabulary.

Visual vocabulary

We computed a visual vocabulary considering a selection of Flickr pictures that stand for different geographic categories in Geonames. We analyzed about 5000 images and extracted a maximum of 1000 Harris-Laplace keypoints per image described by the rotation and scale invariant SIFT descriptors [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF]. Then, we computed a 5000 size codebook with the K-Means algorithm.

To overcome the initialization dependency of the K-Means algorithm we computed ten K-Means with random initializations and retained the best result defined as the partition with the optimal intra-clustered distance. Hörster and al. [START_REF] Horster | Image Retrieval on Large-Scale Image Databases[END_REF] propose a different method based on the merging on multiple K-Means results computed on different subsets of a large-scale collection. In our case, using comfortable calculus capacities and a parallel implementation of the K-Means 3 we could afford to apply the K-Means on the whole dataset. We designate our codebook by W and note each of its visual word by w i .

Indexing process

An image is characterized according to W with a 5000-bins histogram b i h . We extract up to 1000 keypoints with the Harris- Laplace detector and match each point with its nearest w i from W.

Then, each bin of b i h can be seen as the frequency of the visual word w i in the image. As in text mining, we compute the similarity between two images I i and I j described by the bag of features

RESULTS AND EVALUATION

Enrichment of the database

We decided to evaluate our geographical database against an automatically created database (TagMaps) and a manually built thesaurus (Geonames). To do this, we first arbitrarily chose 15 cities from different countries selected to provide a variable quality of the representation in TagMaps and Geonames: Athens, Beijing, Bucharest, Kiev, London, Moscow, Paris, Pittsburgh, San Francisco, Singapore, Sydney, Timisoara, Tokyo, Toulouse and Tunis. For these cities, we ran our geographical candidate selection algorithms, and generated 6000 entities, of which the 20% whose Entity Names appeared 15 times or less on the Web were eliminated because of their low frequency of appearance on the Web.

Instance Extraction and coverage

We evaluated the correctness of the geographic names extraction process for 424 elements generated for our database. For each of the 15 cities, a maximum of 30 randomly extracted items were tested (some cities like Toulouse or Tunis had less than 30 elements discovered). These elements come from both Wikipedia and Panoramio. In the evaluation procedure, we considered as correct all exact matches of the extracted instances to the real names and incomplete matches which are commonly equivalent to their longer forms (i.e. Louvre instead of Louvre Museum). 186) Timisoara ( 1) Tunis ( 7) Kiev ( 8) London (1313) San Francisco (1006) Singapore ( 827) Toulouse ( 10) Tunis ( 24) Timisoara (31) Table 1. Evaluation of the extraction process and coverage.

Correct extractions/

The results in Table 1 show that the extraction process is accurate in over 90% of the cases. Such a precision is to be compared to the 82% precision reported in [START_REF] Rattenbury | Towards Automatic Extraction of Event and Place Semantics from Flickr Tags[END_REF] for TagMaps, the only large scale automatically built geographic database we know of. In the TagMaps case, the authors performed their evaluation after eliminating 50% of their location candidates, starting with the least frequent. Our thresholding of 15 or more web hits only eliminated 20% of our candidates before evaluation. The errors in our approach are due to some imperfections of our named entities extraction. For example, terms like Big House, were mined when using Panoramio and they are reported as errors in this test. We also counted as errors vague terms like Athens Theater, considering that this term covers several geographic objects and is not a geographic name. The coverage outperforms that in TagMaps except for Toulouse area, for which the two methods discover a reduced number of results (10 against 18 in TagMaps). A remarkably high number of results is obtained for cities that are well represented in Wikipedia and in Panoramio. Somewhat surprisingly, although a major tourist destination, Paris does not appear among the best represented cities. The use of an English geographic vocabulary to extract candidates from Panoramio constitutes an explanation for this situation. The internationalization of the vocabulary will further enrich our process. Significant differences can be pinpointed for Kiev (145 location names against 7) or Timisoara (31 against 1).

Instance Categorization

Candidate categorization can be automatically evaluated using the intersection of our database and Geonames for the selected cities: 2

Wikipedia

. Evaluation of the classification process

The results in table 2 show a high rate of success of the overall categorization process. The results for the Wikipedia-based items are consistent with those reported in [START_REF] Kazama | Exploiting wikipedia as external knowledge for named entity recognition[END_REF]; the precision is above 90%. The errors that appear are mainly caused by complicated definitions. For example, the verb to be is sometimes followed by a reference to the geographic situation rather than a direct reference to the instance type. In future work, we plan to add a syntactic analysis means to avoid this type of situation. In Panoramio, the errors appear when the snippets based categorization fails to find the real parent class of a candidate.

Instance Localization

The intersection between Geonames and our database was equally employed so as to assess the distance between common items in both databases. We computed the distribution of the distance between the coordinates in our database and those in Geonames. The distances were quantified every 200 meters. A large majority of the coordinates are distant of no more than 1 kilometer compared to the corresponding manually supplied coordinates in Geonames: -60% of the distances are inferior to 200 meters -81% are inferior to 600 meters -92% are smaller than 1 kilometer -

The highest concentration of results is to be found in the first sector of 200 meters around the Geonames coordinates. As for the differences that are superior to 1 kilometer, they usually appear for items having a large surface (gulf, river, borough or island, university, bay, beach or park). For these geographic objects, a displacement of the geographic coordinates with a distance of the order of 1 kilometer is comparable to their spatial dimensions and does not greatly affect the quality of the representation. A special case is that of rivers, which are objects with a disproportionate ratio between their length and width. One might correctly place their coordinates at any point along their course.

Instance Ranking

Our importance ranking of each geographic name was calculated using two reference corpus, Panoramio and Alltheweb, with a preference given to the results obtained using Panoramio. On the basis of these statistics, we present the top five "most salient" ranked results as for each city : We are not sure how to correctly identify the preferred short form of these names without introducing ambiguities into our automatic system. The example of Timisoara, a city for which the number of Panoramio images is significantly smaller than that for Paris or San Francisco, for which all top ranked terms correspond to landmarks in the area, shows that the ranking method applies well both to well represented regions and to less known areas.

City

Content Based Image Retrieval

Restriction of the query space

The evaluation of content based image retrieval is not a trivial task because it involves the assessment of similarity between pictures as seen by each user. Previous studies [START_REF] Cox | The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments[END_REF], [START_REF] Popescu | Ontology Driven Content Image Retrieval[END_REF] show that the conceptual resemblance of images seems to dominate over their low level similarity (based on perceptual characteristics like color or texture). In ThemExplorer, we propose an interaction schema similar to that in [START_REF] Popescu | Ontology Driven Content Image Retrieval[END_REF], reducing first the region of the database employed in the CBIR query to a conceptually coherent set.

Here we compare the restriction of the query space using localization information (situation 1: S1) and a similar restriction which, in addition, exploits the keywords attached to images (situation 2: S2). In the first situation the CBIR query is performed in image sets representing several entities in the same city, while in second situation, only pictures of the same object are considered. A CBIR process was launched for 20 representative pictures of the objects using S1 and S2 and the 10 closest images were selected. Both punctual entities like (Eiffel Tower or Sydney Opera House) and non-punctual ones (like the Central Park in New York or Bondi Beach in Sydney) were included in the test in order to cover different types of locations. We divided the dataset in two parts (10 objects in each dataset) and asked six information science students (note U1 to U6), with little or no experience in image retrieval, to evaluate the similarity between the example pictures and the answers provided by the CBIR answers. Image similarity is a vague concept and we provided a situation meant to guide the students in their evaluation: they were asked to imagine that they must illustrate a location with a picture and that they found one that seems fitted for this purpose (the example image) but they know that there might be other related images in the database. The task consisted in selecting those answers provided by PIRIA they considered as good substitutes for the query. The presented situation is general enough not to narrow the conclusions of the study to a particular domain and provides some guidance for grasping the notion of similarity. We evaluated the precision using S1 and S2 and present the results for the two sets of images in 4

. Precision of the CBIR when localization information is used to restrict the query (situation S1) and when keywords and localization are employed (S2). The precision of the search process is presented individually (Ui), Avg is the average precision over the users and Inter the intersected precision for all users.

As expected, the results point out that the precision of the CBIR using both keywords and localization information largely outperforms the corresponding results when restricting the query space using only localization information. The two types of restriction generate better results compared to a pure CBIR process and, in addition, reduce the complexity of the search process. The hypothesis we introduced are: if a user selects an image from a region (i.e. a city), she/he only wants to see images of that city (for S1) if she/he selects a picture of a precise object, she/he expects to see similar images depicting the same object (for S2).

Evaluation of the descriptors

We evaluate the performances of the two descriptors we described in section 5. The performance is measured using classical precision and recall metrics. For the test we pick 20 entities and chose one query-image for each one as for the previous evaluation of the influence of the restriction of the query space.

For each query image we gather three series of the ten most similar images return by the CBIR system. The first series corresponds to the global color and texture descriptors (see 5.1.). The second series is the result of the bag of features descriptor (see 5.2.) and the third one is a combining of both features based on a late fusion of the similarity scores. The distance provided using the two descriptors is normalized in [0,1], thus we simply add the similarity values to process the fusion. As for the previous evaluation, for each query and each series, we ask the six students to quote the answer as relevant or not. Because the precision represent a higher interest than the recall in the applications targeted by ThemExplorer we focus the evaluation on precision-based metric: the global precision over the list of the ten answers (P) and the Mean Average Precision (MAP) a classical and very popular metric that enables to consider the rank of the answer in the precision computing.

Global color+texture Bag of features Fusion MAP 0.7473 0.7463 0.8075 P 0.5750 0.5983 0.6983 Table 5

. Evaluation of the descriptors. MAP is the Mean Average Precision (MAP) and P the global precision

Table 5 shows that the performance of the global color and texture descriptor and the bag of features descriptor are close. The interesting information is provided by the gain in both precision and MAP when merging the descriptors through a basic late fusion of the scores. The descriptors are complementary since they focus on different characteristics and patterns providing different relevant answers. In future works, we will continue to experiment new descriptors and merging approaches to improve the CBIR facility in ThemExplorer.

CONCLUSIONS

We built a geographical database, extracting geographic information from heterogeneous information sources on the Web (Wikipedia, Panoramio, Alltheweb) and adding a ranking of the elements in the database. The hierarchical organization of Geonames was exploited in order to introduce a thematic navigation model for geographic image retrieval and we introduced an efficient aggregation of the keyword based search and of a content-based retrieval.

The first results of our system, ThemExplorer, encourage us to continue working in this direction. Our entity extraction approach is especially useful for countries where the representation in Geonames is not well detailed. The CBIR facility based on complementary descriptors enables to find and browse the images more efficiently. As for future work, it would be interesting to adapt the retrieval and the results presentation processes to the type of the queried entity. For example, if a user selects a tag in the natural parks category, the system could show pictures from different places inside this park in order to ensure a more comprehensive view. [START_REF] Kazama | Exploiting wikipedia as external knowledge for named entity recognition[END_REF] introduces an automatic categorization of pictures with respect to their content. The inclusion of this type of algorithms in ThemExplorer can be of help in the adaptation of the results to the type of the queried entity. If pictures for a museum are demanded, pictures from an "interior views" and "exterior views" classes could be displayed. Last, but not the least: we plan to realize a user study for a global validation of the system. The experiments presented in this paper only address only the system components. It would be interesting to have a global assessment of the system performances. The design of the user study is not straightforward but we are aware of the necessity of this type of experimentation. Ideally, the study should include a comparison between ThemExplorer and World Explorer.

ACKNOWLEDGMENT

This work is sponsored by the French research program InfoM@gic.

Figure 1 .

 1 Figure 1. Architecture of ThemExplorer.

Figure 2 .

 2 Figure 2. ThemExplorer interface

Figure 3 .

 3 Figure 3. The 10 best result for the global color and texture-based descriptor. At the top-left, the query image.

Figure 4 .

 4 Figure 4. The 10 best result for the bag of features descriptor. The query image is the same as in figure 3.

Figure 5 .

 5 Figure 5. The 10 best result after fusion. The query image is the same as in figure 3 and 4.

  Coordinate to a candidate geographical entity. A naïve way to categorize geographic entities is to use the category appearing in the name itself. This method, however, fails to correctly categorize instances like the Madison Square Garden in New York or the Palace of Fine Arts in San Francisco, which are not a garden and a palace, but rather a venue and a museum. As shown above, for candidates from Wikipedia, we use the first sentence in the article to determine Entity-Type. For Panoramio candidates, we launch a query with the candidate name in a search engine and analyze the returned snippets. This technique is similar to approaches used in question answering[START_REF] Brill | Dataintensive question answering[END_REF]. The default parent concept of a candidate geographic name is the one appearing in its name. If this candidate is associated more frequently with another geographic concept in the snippets associated to the candidate name, we launch definitional ("candidate IS A concept") on the Web and retain as the final parent concept for the candidate name the one appearing more often in the definitional query. For Wikipedia candidate names not having coordinates, we could use the coordinates of the geo-referenced articles they came from, but these are too imprecise. Instead, we search the names in Panoramio and calculate the average coordinates of the photos whose title contains the analyzed name. The same localization technique is applied to candidate names drawn from Panoramio titles. This simple method produces an approximate localization of the geographic name that, as shown below in the Evaluation section, is satisfying.

These candidates have an Entity-Name, an Entity-Type, but no Entity-Coordinates, yet. To find other geographical names, we use Panoramio in this way: we download photos and analyze their titles. The candidates list includes chains that explicitly contain an element from the geographic vocabulary (i.e. Versailles Castle, Liberty Square). These candidates have an Entity-Name, a potential Entity-Type, and Entity-Coordinates. If missing, we try to assign an Entity-Type and Entity-

Out of total entities tested

	Overall	394/424
	Precision	92.9 %
		Coverage
		TagMaps	ThemExplorer
	Mean / city (σ)	128 (169.5)	405 (377.7)
	Best coverage	London (580)
		San Francisco (472)
		Sydney (
	Worst coverage

Table 3 .

 3 Term rankingThe results in Table4indicate that the ranking procedure introduced in this paper generally succeeds in ranking what seems to be the most representative location names first for the analyzed cities. If we take the example of Paris, we might wonder why Notre Dame does not appear among the first results. The full name of the item in our database is Notre Dame de Paris and this reduces the chances for this item to be found frequently. The same observation stands for Alcatraz in San Francisco, which appears either as Alcatraz Island or Alcatraz Prison.

		Top 5 most salient results
	Athens	Acropolis, Parthenon, Plaka, Olympic
		Stadium, Temple of Zeus
	Beijing	Summer Palace, Temple of Heaven,
		Tiananmen Square, Lama Temple, Railway
		Station
	Bucharest	Intercontinental, Carol Park, Herestrau Park,
		Parliament Palace, Stavropoleos
	Kiev	South Bridge, Trianon Palace, Rusanivka,
		Paton's Bridge, Partizan's Victory Park
	London	London Eye, Tower Bridge, Trafalgar Square,
		Buckingham Palace, Hyde Park
	Moscow	Red Square, Elk Island, Moscow River, St.
		Basil's Cathedral, Historical Museum
	Paris	Louvre, Eiffel Tower, La Défense, Arc de
		Triomphe, Montmartre
	Pittsburgh	PNC Park, Downtown Pittsburgh, Heinz
		Field, Cathedral of Learning, Station Square
	San Francisco	Golden Gate Bridge, Coit Tower, Oakland,
		San Francisco Bay, Lombard Street
	Singapore	Sentosa, Merlion, Raffles Hotel, Singapore
		River, Boat Quay
	Sydney	Opera House, Harbour Bridge, Darling
		Harbour, Bondi Beach, Sydney Tower
	Timisoara	Bega River, Iulius Mall, Unirii Square,

Table 4 :

 4

				Image set 1
		U1	U2	U3	Avg	Inter
	S1	0.27	0.26	0.36	0.29	0.2
	S2	0.58	0.49	0.59	0.55	0.4
				Image set 2
		U4	U5	U6	Avg	Inter
	S1	0.39	0.29	0.21	0.29	0.17
	S2	0.60	0.36	0.43	0.47	0.26
	Table

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