Intensity and color descriptors for texture classification
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1 Intensity and color descriptors for texture classification Claudio Cusano a and Paolo Napoletano b and Raimondo Schettini b a Università degli Studi di Pavia, via Ferrata 1, Pavia, Italy b DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione) Università degli Studi di Milano-Bicocca, viale Sarca 336, Milano, Italy ABSTRACT In this paper we present a descriptor for texture classification based on the histogram of a local measure of the color contrast. The descriptor has been concatenated to several other color and intensity texture descriptors in the state of the art and has been experimented on three datasets. Results show, in nearly every case, a performance improvement with respect to results achieved by baseline methods thus demonstrating the effectiveness of the proposed texture features. The descriptor has also demonstrated to be robust with respect to global changes in lighting conditions. Keywords: Color texture classification, Color contrast map, Illuminant invariant descriptors 1. INTRODUCTION Combinations of color and texture descriptors are a commonplace in texture classification. 1 In fact, studies about human perception suggest that texture and color information are processed separately. 2 However, color descriptors may result misleading in presence of variable lighting conditions. Computational color constancy techniques can not help here, because the samples of the textures to classify lack the context required to estimate the illuminant color. Under this conditions, descriptors obtained solely from the luminance channel tend to outperform those that also take into account color information. In this paper we propose an approach to texture classification based on descriptors obtained from maps of local color contrast. 3 These maps are defined in terms of angles between color vectors in an orthonormal color space. For each pixel of the image, its local color contrast is defined as the angular difference between its own color vector and the average color vector in the surrounding neighborhood. The color contrast maps are invariant with respect to scaling and orthonormal transformations in the color space, and are therefore quite robust with respect to global changes in lighting conditions. In our approach a descriptor is obtained by quantizing the local color contrast and by forming a histogram of the quantized values. The histogram is then combined, using a parallel approach, with several other color and intensity texture descriptors in the state of the art. 2. BASELINE METHODS The purpose of this paper is to show the effectiveness of the proposed color-contrast map for texture classification. For this purpose, we show that adding features coming from a color-contrast coded image to features coming from a color or gray coded image leads to a performance improvement. We selected a number of feature extraction techniques from several classes of approaches: 4, 5 color based, statistical, spatial-frequency or spectral, structural and hybrid. In the following we give a brief description of the considered techniques assuming color images as input. Note that in the case of gray level images we have considered a subset of these techniques specifically modified to work with gray images. 2.1 Normalized color space representation According to this method, the original color image is firstly transformed into a matrix of complex numbers thus reducing the space color from three dimensions to two. Later, the matrix is processed with a bank of Gabor filters, so obtaining 32 rotationally invariant features. 4, 6 Claudio Cusano: claudio.cusano@unipv.it, Paolo Napoletano: napoletano@disco.unimib.it, Raimondo Schettini: schettini@disco.unimib.it Image Processing: Machine Vision Applications VI, edited by Philip R. Bingham, Edmund Y. Lam, Proc. of SPIE-IS&T Electronic Imaging, SPIE Vol. 8661, SPIE-IS&T CCC code: X/13/$18 doi: / SPIE-IS&T/ Vol
2 2.2 Color histogram This method considers a 3-D histogram of an image obtained dividing the color space, the rg-bywb, in parts of equal volume. 7 The number of features is One, two and three marginal histograms This approach is based on a 1-dimensional histogram. In the case of two or three channels the 1-D histogram is obtained by concatenating the histogram of each channel 8 thus obtaining 512 and 768 features respectively. 2.4 Color statistics: mean This is a very simple approach to the problem of texture classification. The feature vector is composed of the average value of each channel in the RGB or rgb space. The number of features is Color statistics: mean, standard deviation and moments The method considers a combination of color statistics: mean, standard deviation and normalized moments from degree three to five. The color spaces are RGB or L*a*b*, therefore the number of features is 5 3 = Color statistics: percentiles Following this approach, three percentiles are considered as features: 11 the first, second and third quartile. The color space is RGB, so the number of features is Chromaticity moments The method defines the trace (T) and the bi-dimensional distribution (D) of a rescaled and discretized version of the chromaticity diagram xy. A feature vector is composed of two sets of five normalized chromaticity moments extracted from the T and D, so obtaining 10 features Co-occurrence matrices This method uses five features extracted from matrices obtained by considering the co-occurrence of color indices. Indices are obtained considering a uniform partition of the RGB space of 64 bins. This approach 13, 14 considers eight co-occurence matrices corresponding to eight displacements. The features, extracted from these matrices, are: contrast, correlation, energy, entropy and homogeneity. 2.9 Integrative Co-occurrence matrices This approach considers a co-occurrence matrix for each color channel separately (3 monochrome features) and for each combination of two color channels (3 couple of opponent features). The method computes five features 13, 15 for each matrix, so we have features Co-occurrence matrices and chromatic features The above co-occurrence features are computed from the luminance plane and combined with chromatic features (consisting in the mean and standard deviation of each chrominance channel). 13 The resulting feature vector is composed of five (monochromatic) and four chromatic elements Co-occurrence matrices and color percentiles Co-occurrence features are computed from the luminance plane and combined with RGB percentiles, 11 described in Section 2.6. The resulting feature vector is composed of 5 (monochromatic) and 9 percentile elements Multilayer coordinate clusters representations The multilayer coordinate clusters representation is based on the histograms of occurrence of binary patterns observed in a 3 3 window (equivalent to 512 possible patterns). Such binary patterns are obtained by considering each image as a stack of layers of binary values. Each layer is associated to a an index of a color. The number of partitions of the RGB space is 27, and considering that a rotationally invariant operator is applied to each layer, we obtain 1872 features. 4 SPIE-IS&T/ Vol
3 2.13 Gabor features A feature extraction method based on Gabor, estimates mean and standard deviation of four orientation features extracted at four frequencies using discrete Fourier Transform applied on each channel of the RGB color space. 4, 16 The total number of features is = Gabor features on Gaussian color model Following this approach, the input image is firstly converted from the RGB model to the Gaussian color model, and later the Gabor feature extraction method, described in Section 2.13, is applied. The number of features is still Gabor features and chromatic features The method consists in a combination of Gabor features extracted, as described in Section 2.13, from the luminance with chromatic features. 18 We have = 32 monochromatic features and 4 chromatic features Opponent Gabor features This method considers Gabor features (as described in 2.13) extracted from several inter-intra channel combinations: monochrome features extracted from each channel separately and opponent features extracted from couple of colors. We have = 96 for monochromatic and for one couple of colors, and =72for the other couple. 19 The total number of features is Complex Wavelet features Dual Tree Complex Wavelet Transform (DT-CWT) has demonstrated to perform better than Discrete Wavelet Transform for texture analysis. 20 The method used here considers four scales, two features (mean and standard 4, 21 deviation), and three color channels, so obtaining 18 features Complex Wavelet features and chromatic features This approach considers a combination of the Dual Tree Complex Wavelet Transform applied on the monochromatic plane with chromatic features. In this case we have 16 features from DT-CWT and 4 chromatic 4, 21 features Granulometry This method applies morphological operators (granulometries) to each color channel separately. Four linear structuring elements with four orientations, transformed with a family of openings and closings operators. The number of features used is LBP and colour percentiles This approach combines Local Binary Patterns (LBP) features extracted from the luminance (36 features) with color percentiles computed on RGB (9 features), as described in Section Histograms of color ratios The method considers ratios between a given pixel and neighbors. The resulting histograms of color ratios (one for each channel) are used as feature vectors. 23 The number of bins for each histogram is 32, therefore the total number of features is Average color differences 4, 22 The method uses variograms for describing the degree of spatial dependence between a pixel and its neighbors. The degree of dependence is measured as the average color difference between a given pixel and neighbor pixels located on given displacements associated to four variograms. The size of the feature vector is 50. SPIE-IS&T/ Vol
4 3. THE COLOR CONTRAST DESCRIPTOR We assume that the pixels are represented by triplets c =(c 1,c 2,c 3 ) of non-negative values, and in the experiments these values will correspond to the red, green, and blue components in the RGB color space. This descriptor can be easily generalized to other color spaces. To make the descriptor robust against changes in the color of the illuminant, the local color contrast is computed in terms of angular differences between vectors in the color space. Given two vectors c 1, c 2, the corresponding angular difference is defined as: { 2 π c1,c2 arccos c α(c 1, c 2 )= 1 c 2 if c 1 0 c 2 0 (1) 0 otherwise, where, denotes the inner product in the color space, and where indicates the Euclidean norm. Note that 0 α 1 because the vectors have non-negative components. The local contrast is obtained by measuring the angular difference between the color of the pixel c(i, j) atthelocationi, j and the average normalized color c(i, j) of its neighborhood: c(i, j) = 1 (2W +1) 2 i+w j+w x=i W y=j W c(x, y) c(x, y), (2) where the summation is taken only over the non-zero vectors, and where W determine the size of the neighborhood. The normalization of the terms in the summation makes the average invariant with respect to scaling in the color space. Figure 3 shows some examples of color contrast maps, that is, the images of the angular differences α(c(i, j), c(i, j)), obtained with different values of W : small values produce detailed contrast maps, while the coarse structure of the texture is captured when W is large. The distribution of the angular difference in the contrast map has been uniformly quantized into 256 bins and encoded by a histogram, that we called Color Contrast Histogram. The final descriptor has been obtained by the concatenation of the color contrast histogram to the features obtained using one of the methods described in Section EXPERIMENTAL RESULTS We evaluated the use of the proposed color contrast map on three different data sets. For each data set we measured the performance of a nearest neighbor classifier, using the L 1 distance as in the work of Bianconi et al., 4 on a single color or intensity feature, and on the combination of that feature with the histogram of color contrast obtained with different values of W. We considered, for all the baseline methods, MATLAB implementations described by Bianconi et al. 4 and available on line. 24 The first experiment has been conducted on the VisTex 25 data set. This data set consists of 864 images representing 54 classes of natural objects or scenes captured under non-controlled conditions with a variety of devices (Figure 1). Each photograph has been divided into 16 sub-images, half of which has been included in the training set; the remaining sub-images form the test set. Table 1 reports the results obtained with the color features. Only in four cases the introduction of the color contrast map did not improved the results. The improvement caused by the contrast map is more evident for the intensity features, as reported in Table 2. For the second experiment we considered the 1360 images of the Outex 13 data set. 26 The photographs depict the textures of 68 different materials, and have been obtained under controlled condition (same illumination, same acquisition device). Figure 2 shows an example for each of the 68 classes. Tables 3 and 4 report the results obtained on this data set by using color and intensity features. We can observe a behaviour similar to that of the previous experiment: the introduction of the color contrast map led to a general improvement of the classification accuracy which is particularly evident in the case of intensity features. The third data set (Outex 14) is an extension of Outex 13: the training set is the same, but the test set is formed by 1360 images obtained from additional photographs of the same 68 classes taken under different lighting conditions (different illuminant color, and slightly different position of the light source). The variable illumination makes color information less useful, if not misleading. The color contrast map allows to keep some color information without being too sensitive with respect to changes in illumination. For instance, Figure 3 shows how the differences in illumination conditions are not reflected in the contrast maps. This fact is also confirmed by the classification accuracies reported in Table 5 and Table 6. For this data set the best results have been obtained by using intensity features combined with statistics on the color contrast map. SPIE-IS&T/ Vol
5 Features Without map W =3 W =5 W =10 W =20 average colour differences chromaticity moments colour histogram colour statistics mean rgb colour statistics mean RGB colour statistics mean std moments Lab colour statistics mean std moments RGB colour statistics percentiles RGB cooccurrence matrices cooccurrence matrices and colour percentiles cooccurrence matrices and croma features dt cwt dt cwt and chroma features gabor gabor and chroma features gabor features on gaussian colour model granulometry histograms of colour ratios integrative cooccurrence matrices LBP and colour percentiles multilayer CCR normalized colour space representation one marginal histogram H opponent gabor features three marginal histograms I1I2I three marginal histograms rgb three marginal histograms RGB two marginal histograms H V Table 1. Classification accuracy obtained on the VisTex data set by using color features with and without combination Features Without map W =3 W =5 W =10 W =20 cooccurrence matrices and intensity percentiles dt cwt gabor features gabor features on gaussian intensity model granulometry integrative cooccurrence matrices one marginal histogram intensity Table 2. Classification accuracy obtained on the VisTex data set by using intensity features with and without combination SPIE-IS&T/ Vol
6 Features Without map W =3 W =5 W =10 W =20 average colour differences chromaticity moments colour histogram colour statistics mean rgb colour statistics mean RGB colour statistics mean std moments Lab colour statistics mean std moments RGB colour statistics percentiles RGB cooccurrence matrices cooccurrence matrices and colour percentiles cooccurrence matrices and croma features dt cwt dt cwt and chroma features gabor gabor and chroma features gabor features on gaussian colour model granulometry histograms of colour ratios integrative cooccurrence matrices LBP and colour percentiles multilayer CCR normalized colour space representation one marginal histogram H opponent gabor features three marginal histograms I1I2I three marginal histograms rgb three marginal histograms RGB two marginal histograms H V Table 3. Classification accuracy obtained on the OuTex 13 data set by using color features with and without combination Features Without map W =3 W =5 W =10 W =20 cooccurrence matrices and intensity percentiles dt cwt gabor features gabor features on gaussian intensity model granulometry integrative cooccurrence matrices one marginal histogram intensity Table 4. Classification accuracy obtained on the OuTex 13 data set by using intensity features with and without combination SPIE-IS&T/ Vol
7 Features Without map W =3 W =5 W =10 W =20 average colour differences chromaticity moments colour histogram colour statistics mean rgb colour statistics mean RGB colour statistics mean std moments Lab colour statistics mean std moments RGB colour statistics percentiles RGB cooccurrence matrices cooccurrence matrices and colour percentiles cooccurrence matrices and croma features dt cwt dt cwt and chroma features gabor gabor and chroma features gabor features on gaussian colour model granulometry histograms of colour ratios integrative cooccurrence matrices LBP and colour percentiles multilayer CCR normalized colour space representation one marginal histogram H opponent gabor features three marginal histograms I1I2I three marginal histograms rgb three marginal histograms RGB two marginal histograms H V Table 5. Classification accuracy obtained on the OuTex 14 data set by using color features with and without combination Features Without map W =3 W =5 W =10 W =20 cooccurrence matrices and intensity percentiles dt cwt gabor features gabor features on gaussian intensity model granulometry integrative cooccurrence matrices one marginal histogram intensity Table 6. Classification accuracy obtained on the OuTex 14 data set by using intensity features with and without combination SPIE-IS&T/ Vol
8 Figure 1. One sample for each of the 54 classes in the VisTex data set. 5. CONCLUSIONS In this work we presented a descriptor for texture classification based on the histogram of a local measure of the color contrast defined in terms of the angle between the color of the pixels and the average color of their neighborhoods. The resulting color contrast histogram does not encode any information related to the luminance of the image and is therefore insufficient for an effective classification of the textures. However, its addition to traditional features leads to a remarkable improvement in terms of classification accuracy. This fact is demonstrated by our experiments on the VisTex and the Outex data sets. While the first dataset includes textures of natural objects or scenes, each one taken under the same illuminant, the second is composed of textures of different materials acquired under different illumination conditions. By combining the color contrast histogram with a large number of texture features from the state of the art (based both on color and intensity) we observed a consistent improvement in the classification performance. In particular, the improvement in the third experiment is quite evident (an average improvement of classification accuracy of about 33% for color features and 24% for gray level features). Note that among the experiments considered, the third is the only one that includes images having change in illumination; the results obtained confirm the robustness of our descriptor in dealing with this type of variability. REFERENCES [1] Ma nepa a, T. and Pietika inen, M., Classification with color and texture: jointly or separately?, Pattern Recognition 37(8), (2004). [2] Poirson, A. B. and Wandell, B. A., Pattern-color separable pathways predict sensitivity to simple colored patterns, Vision Research 36, (1996). [3] Cusano, C., Napoletano, P., and Schettini, R., Illuminant invariant descriptors for color texture classification, in [Computational Color Imaging ], Schettini, R., Tominaga, S., and Tre meau, A., eds., Lecture Notes in Computer Science 7786, , Springer Berlin Heidelberg (2013). SPIE-IS&T/ Vol
9 Figure 2. The 68 classes considered in the second and third classification experiments. [4] Bianconi, F., Harvey, R., Southam, P., and Ferna ndez, A., Theoretical and experimental comparison of different approaches for color texture classification, Journal of Electronic Imaging 20(4) (2011). [5] Mirmehdi, M., Xie, X., and Suri, J., [Handbook of Texture Analysis ], Imperial College Press, London, UK (2008). [6] Vertan, C. and Boujemaa, N., Color texture classification by normalized color space representation, in [Pattern Recognition, Proceedings. 15th International Conference on ], 3, vol.3 (2000). [7] Swain, M. J. and Ballard, D. H., Color indexing, International Journal of Computer Vision 7, (1991). [8] Pietikainen, M., Nieminen, S., Marszalec, E., and Ojala, T., Accurate color discrimination with classification based on feature distributions, in [Pattern Recognition, 1996., Proceedings of the 13th International Conference on ], 3, vol.3 (aug 1996). [9] Kukkonen, S., Kalviainen, H., and Parkkinen, J., Color features for quality control in ceramic tile industry, Optical Engineering 40(2), (2001). [10] Lo pez, F., Valiente, J., Baldrich, R., and Vanrell, M., Fast surface grading using color statistics in the cie lab space, in [Pattern Recognition and Image Analysis ], Marques, J., Pe rez de la Blanca, N., and Pina, P., eds., Lecture Notes in Computer Science 3523, , Springer Berlin Heidelberg (2005). SPIE-IS&T/ Vol
10 W =3 W =5 W =10 W =20 W =3 W =5 W =10 W =20 Figure 3. Color contrast maps obtained on images from six of the 68 classes of the Outex 14 data set, taken under two different illumination conditions. The maps have been computed by using four different values of W. [11] Niskanen, M., Silvén, O., and Kauppinen, H., Color and texture based wood inspection with non-supervised clustering., (2001). Proc. 12th Scandinavian Conference on Image Analysis, June 11-14, Bergen, Norway, [12] Paschos, G., Fast color texture recognition using chromaticity moments, Pattern Recognition Letters 21(9), (2000). [13] Arvis, V., Debain, C., Berducat, M., and Benassi, A., Generalization of the cooccurrence matrix for colour images: Application to colour texture, Image Analysis & Stereology 23(1) (2004). [14] Hauta-Kasari, M., Parkkinen, J., Jaaskelainen, T., and Lenz, R., Generalized co-occurrence matrix for multispectral texture analysis, in [Pattern Recognition, 1996., Proceedings of the 13th International Conference on], 2, vol.2 (aug 1996). [15] Palm, C., Color texture classification by integrative co-occurrence matrices, Pattern Recognition 37(5), (2004). [16] Bianconi, F. and Fernández, A., Evaluation of the effects of gabor filter parameters on texture classification, Pattern Recognition 40(12), (2007). [17] Hoang, M. A., Geusebroek, J.-M., and Smeulders, A. W. M., Color texture measurement and segmentation, Signal Process. 85, (Feb. 2005). [18] Drimbarean, A. and Whelan, P., Experiments in colour texture analysis, Pattern Recognition Letters 22(10), (2001). [19] Jain, A. and Healey, G., A multiscale representation including opponent color features for texture recognition, Image Processing, IEEE Transactions on 7, (jan 1998). [20] Kingsbury, N., Image processing with complex wavelets, Phil. Trans. Royal Society London A 357, (1997). [21] Barilla, M. and Spann, M., Colour-based texture image classification using the complex wavelet transform, in [Electrical Engineering, Computing Science and Automatic Control, CCE th International Conference on], (nov. 2008). [22] Hanbury, A., Kandaswamy, U., and Adjeroh, D., Illumination-invariant morphological texture classification, in [Mathematical Morphology: 40 Years On], Ronse, C., Najman, L., and Decencire, E., eds., Computational Imaging and Vision 30, , Springer Netherlands (2005). [23] Gevers, T. and Smeulders, A. W. M., A comparative study of several color models for color image invariant retrieval, in [In Proceedings of the first international workshop on Image databases and multi-media search (IDB-MMS 96], (1996). SPIE-IS&T/ Vol
11 [24] Bianconi, F., Baseline methods implementation. [25] MIT Media Lab, Vision texture homepage. VisionTexture/. [26] Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllönen, J., and Huovinen, S., Outex-new framework for empirical evaluation of texture analysis algorithms, in [16th International Conference on Pattern Recognition], 1, (2002). SPIE-IS&T/ Vol
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