Combination of LBP Bin and Histogram Selections for Color Texture Classification

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently

Color texture feature selection for image classification : Application to flaw identification on decorated glasses printing by a silk-screen process

Dans le cadre du contrôle qualité de décors verriers par analyse d’image, nous proposons une méthodologie originale de classification supervisée de textures couleur pour identifier les défauts d’aspect présents sur les décors. Cette méthodologie consiste à construire un espace d’attributs de texture couleur discriminant de dimension réduite lors d’un apprentissage hors ligne afin d’y représenter les textures à classer en ligne. Afin de satisfaire aux contraintes exigées par une application industrielle en termes de qualité de résultats et de temps de calcul, l’originalité de notre approche consiste à sélectionner automatiquement un nombre réduit d’attributs qui, d’une part sont évalués à partir d’images codées dans plusieurs espaces couleur exploitant des propriétés différentes et d’autre part, tiennent compte des relations spatiales intra et inter-composantes existant au sein de ces espaces. Nous montrons alors que les indices d’Haralick extraits des matrices de co-occurrences chromatiques sont des attributs répondant à ces objectifs lorsque le nombre de couleurs de l’image est réduit grâce à une sous-quantification des composantes couleur et qu’un voisinage isotropique est utilisé. L’approche proposée est d’abord appliquée à trois bases d’images de textures couleur de référence afin de montrer l’apport de l’approche multi-espaces couleur et le bénéfice que présente la sélection d’attributs, avant d’être appliquée au contrôle qualité des décors verriers. Pour répondre au problème de sous-représentativité des prototypes lié à cette application, nous introduisons une approche originale basée sur la génération d’images de synthèse présentant les défauts à détecter.In the framework of decorated glasses quality control by image analysis, we propose an original methodology of supervised color texture classification in order to identify aspect flaws on patterns. This methodology consists in determining a low dimensional discriminating color texture feature space during an off-line learning stage in order to perform an on-line texture classification in this selected feature space. To satisfy constraints required by industrial applications about processing time and quality of texture classification, the originality of our approach consists in automatically selecting a reduced number of features which, on one hand are evaluated from images coded in several color spaces with different properties and on the other hand, which take into account the spatial relationships within and between the color components of each space. Then, we show that Haralick features extracted from color cooccurrence matrices answer to these goals when the number of colors in the image is reduced thanks to a quantization of color components and when an isotropic neighborhood is used. The proposed approach is firstly applied on three color texture benchmark databases in order to show the contribution of the multi color space approach and the advantage of feature selection. The method is then applied to decorated glasses quality control. In order to answer to the lack of prototypes, we introduce an original approach based on the generation of synthetic images where aspect flaws are present

Haralick feature extraction from LBP images for color texture classification

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Comparison of color imaging vs. hyperspectral imaging for texture classification

International audienceMany approaches of texture analysis by color or hyperspectral imaging are based on the assumption that the image of a texture can be viewed as a multi-component image, where spatial interactions within and between components are jointly considered (opponent component approach) or not (marginal approach). When color images are coded in multiple color spaces, texture descriptors are based on Multi Color Channel (MCC) representations. By extension, a Multi Spectral Band (MSB) representation can be used to characterize the texture of material surfaces in hyperspectral images. MSB and MCC representations are compared in this paper for texture classification issues. The contribution of each representationis investigated with marginal and/or opponent component strategies. For this purpose, several relevant texture descriptors are considered. Since MSB and MCC representations generate high-dimensional feature spaces, a dimensionality reduction is applied to avoid the curse of dimensionality. Experimental results carried out on three hyperspectral texture databases (HyTexiLa, SpecTex and an original dataset extracted from the Timbers database) show that considering between component interactions in addition to the within ones significantly improves the classification accuracies. The proposed approaches allow also to outperform state of the art hand-designed descriptors and color texture descriptors based on deep learning networks. This study highlights the contribution of hyperspectral imaging compared to color imaging for texture classification purposes but also the advantages of color imaging depending on the considered texture representatio

Neighborhood and Haralick feature extraction for color texture analysis

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Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection

A new benchmark image test suite for evaluating color texture classification schemes

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Color Texture Attributes

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Compact Hybrid Multi-Color Space Descriptor Using Clustering-Based Feature Selection for Texture Classification

International audienceColor texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional approaches, these descriptors are used with a predefined setting of their parameters and computed from images coded in a chosen color space. The prior choice of a color space, a descriptor and its setting suited to a given application is a crucial but difficult problem that strongly impacts the classification results. To overcome this problem, this paper proposes a color texture representation that simultaneously takes into account the properties of several settings from different descriptors computed from images coded in multiple color spaces. Since the number of color texture features generated from this representation is high, a dimensionality reduction scheme by clustering-based sequential feature selection is applied to provide a compact hybrid multi-color space (CHMCS) descriptor. The experimental results carried out on five benchmark color texture databases with five color spaces and manifold settings of two texture descriptors show that combining different configurations always improves the accuracy compared to a predetermined configuration. On average, the CHMCS representation achieves 94.16% accuracy and outperforms deep learning networks and handcrafted color texture descriptors by over 5%, especially when the dataset is small