A fast embedded selection approach for color texture classification using degraded LBP

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1 A fast embee selection approach for color texture classification using egrae A. Porebski, N. Vanenbroucke an D. Hama Laboratoire LISIC - EA Université u Littoral Côte Opale - 50, rue Ferinan Buisson - BP Calais Ceex - FRANCE porebski@lisic.univ-littoral.fr, vanenbroucke@lisic.univ-littoral.fr, enis.hama@lisic.univ-littoral.fr Abstract We propose a fast embee selection approach for color texture classification using Local Binary Pattern (). This texture escriptor transforms an image by thresholing the neighborhoo of each pixel an coing the result as a binary number. The selection approach presente in this paper is base on a egrae efinition of the color s. To compute these egrae s, we take care of choosing a relevant reuce neighborhoo - or a combination of reuce neighborhoos - with respect to the analyse textures. This leas to consier histograms with a lower imension an so to reuce the computation times. We thus propose to etermine the imension of the selecte feature subspace with these egrae color s an to use this imension for the classification with the classic s. Experimental results carrie out with benchmark atabases in ifferent color spaces show that this approach allows to obtain such goo classification results than when the basic efinition of is use, while significantly reucing the learning time. Keywors Embee selection,, Histogram selection, Color texture, Supervise classification. I. INTRODUCTION Texture classification is a research topic that ha le in recent ecaes to many stuies for various image processing applications. In this framework, several authors have shown that taking into account both the spatial arrangement of the colors in the image plane an the color istribution in the color space outperforms the texture classification result provie by the analysis of gray levels [1], [2]. However, the use of color leas, on one han, to choose a well-suite color space in which the textures are escribe an, on the other han, to consier a higher imensional feature space [3]. Because of the curse of imensionality, most of the stuies perform a selection in orer to buil a lower imensional feature subspace in which a classifier operates [4], [5], [6]. By only selecting the most iscriminant features, these approaches aim to improve the classification result, while ecreasing the classification computation time an the storage requirements. Different approaches are propose in orer to select a low imensional feature subspace [7]. Wrapper approach is a feature selection proceure that uses the classification rate as a iscrimination power of a feature subspace. It nees to classify all the images of a learning atabase for all the caniate feature subspaces, that involves an important learning time an classifier-epenent results. However, this approach gives goo results an allows to easily etermine the imension of the feature subspace by searching the best classification rate. Contrary, filter approaches are feature selection proceures that evaluate the iscrimination powers of ifferent caniate feature subspaces without classifying the images. They are less time consuming but suffer to the ifficulty to etermine the imension of the feature subspace to be selecte. To obtain a goo compromise between imension selection, computation time an classification result, embee approaches are preferre [8]. These approaches combine a filter approach to etermine the most iscriminating feature subspaces at ifferent imensions an a wrapper approach to etermine the imension of the selecte subspace [9]. Because of the wrapper step, the learning is still time consuming. In this paper, we aim to reuce the computation time of the wrapper step in the context of color texture classification using the Local Binary Pattern (). The color is wiely use as texture escriptor for classification of color images [10]. It transforms an image by thresholing the neighborhoo of each pixel an coing the result as a binary number. The approach presente in this paper uses a fully trie an teste embee histogram selection approach [11] an proposes a egrae color to faster etermine the imension of the selecte feature subspace. This egrae version of color consists in consiering a relevant reuce neighborhoo or a combination of reuce neighborhoos to compute histograms. Once the imension is etermine with the egrae, we use this imension for the classification with the basic efinition of. In the following, we first propose to escribe the egrae color histograms (see section II). Then, the propose fast embee selection approach is presente in section III an finally applie an teste with 4 ifferent color spaces on benchmark atabases in section IV. In this last section, the classification accuracies an the computation times obtaine by the propose approach will be compare with those obtaine in [11]. II. DEGRADED COLOR HISTOGRAMS A. Color histograms The operator has initially been propose in 1996 by Ojala et al. to escribe the textures present in gray level images [12]. It has then been extene to color by Mäenpää et al. an use in several color texture classification problems [10], [1]. The color of pixels is usually coe by three components represente in a 3-imensional color space, enote here

2 C 1 C 2 C 3. In orer to characterize the whole color texture image, the operator is applie on each pixel an for each pair of components (C k,c k ), k,k {1,2,3}. Consiering a pair of components, the color operator consists in assigning to each pixel a label which characterizes the local pattern in a neighborhoo. Each label is calculate by thresholing the color component of the neighbors by using the color component of the consiere pixel. The result of the thresholing, performe for each neighboring pixel, is then coe thanks to a weight mask. In the basic version of the operator, the 3 3 neighborhoo is consiere (see figure 1 (a)). This 8-neighborhoo leas to characterize the value of each pixel by an 8-bits integer a) 8-neighborhoo 3x3 an the corresponing weight mask b) Axial 4-neighborhoo an the corresponing weight mask b) Diagonal 4-neighborhoo an the corresponing weight mask Fig. 1. Neighborhoos use to compute an their corresponing weight masks. The corresponing istributions are represente in nine ifferent histograms, integrating the global color texture information: three within-component histograms ((C 1,C 1 ), (C 2,C 2 ) an (C 3,C 3 )) an six between-component histograms ((C 1,C 2 ),(C 2,C 1 ), (C 1,C 3 ),(C 3,C 1 ),(C 2,C 3 ) an (C 3,C 2 )). A color texture is thus characterize in a (9 256)- imensional feature space. In this paper, we propose to select among these nine 256- imensional histograms those which are the most iscriminating ones uring the learning stage of the classification process. In orer to spee up the wrapper step of this learning stage, a egrae efinition of the is use. B. Choice of the neighborhoo Consiering a reuce neighborhoo to compute the color allows to reuce the computation time. Inee, as shown on the figure 1 (b), the imension of the histogram epens on the weight mask an so on the number of consiere neighbors. 8 neighbors give a 2 8 = 256-imensional histogram an 4 neighbors give a 2 4 = 16-imensional histogram. In practice, the choice of the reuce neighborhoo epens on the analyse textures [13]. That is the reason why we choose: the axial 4-neighborhoo for textures which mainly contain vertical an/or horizontal patterns, the iagonal 4-neighborhoo for textures which mainly contain iagonal patterns. For the textures which o not present a specific irection, we propose to combine the two 4-neighborhoos. This original combination, one by concatenating the two resulting histograms, is very relevant since it allows to analyse all the 8 neighbors of a pixel, but with a (2 16)-imensional histogram instea of a 256-imensional histogram. Using a reuce neighborhoo to compute the color thus presents the avantage of ecreasing the computation time. However, the rates of well-classifie images can be egrae. To take avantage of a reuce computation time without lowering too much the final classification result, we propose to use this egrae efinition of s only uring the wrapper step of the propose embee selection approach, as explaine in the next section. III. FAST EMBEDDED SELECTION The analysis of the color textures thanks to basic color histograms involves to represent images in a (9 256)- imensional feature space. Several approaches have been propose to reuce the imension of such a feature space. Mäenpää et al. consier opponent color s. Some authors select the most iscriminant bins which constitute the histograms [10]. Unlike to classic bin selection, we have propose in a previous stuy another approach, which selects, out of the nine histograms extracte from a color texture, those which are the most iscriminant for the consiere application [11]. This approach has allowe a significant result improvement compare to the without selection. This histogram selection approach consists in selecting, uring a supervise training stage, a iscriminant subspace in which the classifier operates uring a ecision stage. For this purpose, we apply a holout ecomposition to the initial image ataset in orer to buil a training image subset an a testing image subsets. A measure of the histogram relevance, base on a within-class similarity measure, is first compute. This score is calculate for each caniate histogram thanks to the training images. The histogram intersection is use to evaluate the similarity between the histograms extracte from images of a same class. Let I k j be the kth training image of the class j out of the N j available ones, H be the caniate histogram to evaluate, h be the corresponing normalize histogram 1 an Q be the number of histogram bins. The histogram intersection measure is efine as follows: Q D(Ij k,ik j ) = min(h[ij k ](i),h[ik j ](i)). (1) i=1 1 To normalize the histogram, the number of count in each bin is ivie by the total count, so that the normalize values sum to 1 across all bins.

3 To measure the within-class similarity of a texture class j, the measure SIMj is consiere: SIMj = Nj 1 Nj X X 2 D(Ijk, Ijk ). Nj (Nj 1) (2) The BarkTex test suite3 inclues 6 tree bark classes, with 136 images per class. This image set has been built so that color texture images use as training an testing images are less correlate as possible [17]. Figures 2, 3 an 4 illustrate some textures of these three color texture sets. k=1 k =k+1 We suppose that the higher the measure SIMj of within-class similarity is, the more relevant the histogram H is. The score S, which inclues all within-class similarities, is thus efine as follows: S= M 1 X SIMj, M j=1 (3) where M is the number of consiere classes. The most iscriminant histogram maximizes the score S. Inee, we seek in this paper the representation that minimizes the withinclass variation. This score S is compute for each histogram uring the filter step an a feature ranking algorithm is performe. In orer to etermine the imension ˆ of the selecte feature subspace, a wrapper approach then evaluates the 9 caniate subspaces compose of the first ranke histograms ( {1,..., 9}). We propose to measure the rate R of well-classifie testing images obtaine with each -imensional caniate subspace an to use these rates to evaluate the performances of the propose approach in the next section. These rates are obtaine thanks to the nearest neighbor classifier associate with the histogram intersection as a similarity measure. The selecte subspace is the caniate subspace for which R is maximum: ˆ = argmax R. Fig. 2. Example of OuTex color textures: each image represents a class of texture. (4) 1 9 It is at this wrapper step that we propose to consier the egrae color histograms previously presente. Inee, this step is the most computationally expensive since several classifications are one to etermine the imension ˆ of the most relevant subspace. IV. E XPERIMENTS We propose to apply the propose approach on three benchmark color texture atabases: OuTex, VisTex an BarkTex [14], [15], [16]. Most of the authors who have assesse the efficiency of color texture classification algorithms, have use image test suites extracte either from the OuTex atabase or the VisTex one. Out of these ifferent sets, the OuTex-TC an Contrib-TC test suites2 are consiere as benchmark atabases for comparing performances. OuTex-TC is compose of 68 classes of material textures with 20 images per class whereas Contrib-TC contains 54 classes of natural textures with 16 images per class. 2 Fig. 3. Example of VisTex color textures: each image represents a class of texture. In orer to show the interest of the propose fast embee selection approach for color texture classification, four color spaces are consiere for experiments: RGB, Y U V, I1 I2 I3 an HSV. They are representative of the four color space 3 The BarkTex image test suite can be ownloae at porebski/ BarkTex_image_test_suite.html

4 85% 75% 65% 55% R Without selection % RGB - Degrae RGB - Fig. 4. Example of BarkTex color textures: each column represents a class of texture. 80% 70% 60% 50% 40% 30% 20% R % YUV - Degrae YUV - families (primary, luminance-chrominance, perceptual an inepenent color component spaces) an o not require to know illumination an acquisition conitions. First, we propose in sections IV-A an IV-B to etail the results obtaine by our selection approach on the color texture atabases OuTex, VisTex an BarkTex. We then iscuss the computation time in section IV-C. A. Dimension of the feature space We notice that the textures of OuTex an VisTex o not present a specific irection whereas the BarkTex textures mainly contain vertical patterns. To compute the egrae color, we thus combine the two reuce 4- neighborhoos for OuTex an VisTex an we consier the axial 4-neighborhoo for the BarkTex set. Figure 5 shows, for the BarkTex set an for the ifferent color spaces, the evolution of the rate R (%) of wellclassifie testing images accoring to the imension of the consiere subspace, when a basic an a egrae color are consiere in the wrapper step. We notice that the curves obtaine by consiering basic or egrae color s have similar trens, whatever the consier color space. We thus propose to etermine the imension ˆ (otte line) of the selecte feature subspace by using the egrae color (soli curve) uring the wrapper step, an to use this imension ˆ with the basic (ashe curve) for the classification. Table 1 compares the imension ˆ etermine by consiering the basic or the egrae color uring the wrapper step of the selection scheme. In 9 cases out of 12, the imension ˆ obtaine with the egrae color is the same than the one etermine with the basic color. In the other cases, it is the imension that gives the secon best rate with the basic color which is foun. It shoul be note that there is no rule which can be euce about the histograms which are the most iscriminating. Inee, in [11], it is shown that the within-component histograms or the between-component ones can be first selecte an that consiering together the cross correlations features 80% 70% 60% 50% 40% 30% 20% 10% 85% 75% 65% 55% 45% R R % % I1I2I3 - Degrae I1I2I3 - HSV - Degrae HSV - Fig. 5. Evolution of the rate R (%) of well-classifie testing images accoring to the imension of the consiere subspace ( = 9 in the case where there is no selection). Table 1. Comparison of the imensions ˆ of the selecte subspaces with the OuTex VisTex BarkTex basic or the egrae color. RGB YUV I 1I 2I 3 HSV Basic Degrae Basic Degrae Basic Degrae like (C 1,C 2 ) an(c 2,C 1 ) allows to improve the classification results compare to opponent color s which only consier the three within-component histograms an three out of six between-component histograms. B. Classification results Table 2 shows the rates Rˆ of well-classifie testing images reache with the propose egrae color approach, for the OuTex, VisTex an BarkTex sets. RGB is often consiere as the color space that oes not provie goo classification performances. But as we can see in our case or in other stuies where the performances of

5 Table 2. Rates Rˆ of well-classifie testing images reache with the propose egrae color approach, for the OuTex, VisTex an BarkTex sets. Color space OuTex VisTex BarkTex RGB 92.50% 98.84% 81.37% Y UV 89.56% 98.38% 79.17% I 1I 2I % 97.92% 79.41% HSV 91.03% 97.69% 81.00% several color spaces are compare ([18], [19], [20]), RGB can sometimes give better classification results than the other color spaces. It confirms that the best color space epens on the consiere application [3]. These results also show that the propose approach that fast etermines the imension ˆ remains effective whatever the consiere color space. Furthermore, table 1 has shown that in 9 cases out of 12, the imension ˆ obtaine with the egrae color is the same as the one etermine with the basic color an that in the 3 other cases, it is the imension that gives the secon best rate with the basic color which is foun. The classification result is thus slightly impacte compare to the basic approach presente in [11]: -0.37% on average for OuTex an -0.23% for VisTex when the etermine imensions are not the same. Finally, the analysis of the classification rates presente in figure 5 shows that processing a selection allows to improve the classification results. Inee, the improvement is on average 8.4% compare to the without selection step for the BarkTex set. C. Computation time The previous section has shown that the propose approach almost gives as goo results as those obtaine with the full neighborhoo. In orer to show the benefit of consiering a egrae color, we propose to stuy the computation time of learning stage with the BarkTex set, when the basic or the egrae color is consiere. Theses times are etaile in table 3. Table 3. Learning computation times obtaine with the BarkTex set. Basic Degrae Histogram extraction with 7.31 s 7.31 s the 8-neighborhoo Filter selection 2.48 s 2.48 s Histogram extraction with none 3.67 s the axial 4-neighborhoo Wrapper selection s 6.94 s Total s 20.4 s As we have previously seen, the propose approach firstly consiers a filter selection approach to buil several caniate feature subspaces compose of basic color. The computation time neee to extract the basic color from the learning BarkTex images is 7.31 s an the time require to buil the caniate feature subspaces with the ranking algorithm is equal to 2.48 s. In [11], no egrae is consiere. The time to extract histograms with the axial 4- neighborhoo is thus 0 s. But the cost of the classifications neee to select the imension ˆ is very high: s. In the approach propose in this paper, we consier a egrae color uring the wrapper step. We thus nee to extract histograms with the axial 4-neighborhoo, that costs 3.67 s more than the basic approach. However, the time to etermine the imension ˆ is significantly reuce: 6.94 s instea of s. So, with the classic approach propose in [11], the learning time obtaine with the BarkTex set is about 86 s. Consiering the egrae efinition of to etermine the imension ˆ allows to obtain a learning time of 21 s. This egrae approach allows thus to give as goo classification result, but with a 4 times faster learning stage. V. CONCLUSION This paper presents a fast embee selection approach for color texture classification. It consists in consiering a reuce neighborhoo or a combination of reuce neighborhoos to compute histogram uring the wrapper step of the selection proceure. The results show that processing a selection allows to improve the classification rate an that the propose approach almost gives as goo results as those obtaine with the full neighborhoo while iviing by 4 the learning time. This approach is thus appropriate to reuce the high imensional feature space generate by the analysis of the color an the texture. In a future work, we will exploit this fast embee selection for a multi-space approach, where several color spaces are conjointly consiere. This multi-space approach which gives encouraging results is time consuming. The time saving brought by our fast embee selection proceure being proportional to the number of consiere features, we expect a high learning time saving for the selection in the case of the multi-space approach. REFERENCES [1] T. Mäenpää an M. Pietikäinen, Classification with color an texture: jointly or separately?, Pattern Recognition Letters, 37(8): , [2] C. Palm, Color texture classification by integrative co-occurrence matrices, Pattern Recognition Letters, 37(5): , [3] A. Porebski, N. Vanenbroucke an L. 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