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Theses

Sélection d'attributs multi-espace à partir de motifs binaires locaux pour la classification de textures couleur

Abstract : Texture analysis has been extensively studied and a wide variety of description approaches have been proposed. Among them, Local Binary Pattern (LBP) takes an essential part of most of color image analysis and pattern recognition applications. Usually, devices acquire images and code them in the RBG color space. However, there are many color spaces for texture classification, each one having specific properties. In order to avoid the difficulty of choosing a relevant space, the multi color space strategy allows using the properties of several spaces simultaneously. However, this strategy leads to increase the number of features extracted from LBP applied to color images. This work is focused on the dimensionality reduction of LBP-based feature selection methods. In this framework, we consider the LBP histogram and bin selection approaches for supervised texture classification. Extensive experiments are conducted on several benchmark color texture databases. They demonstrate that the proposed approaches can improve the state-of-the-art results.
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Submitted on : Tuesday, April 3, 2018 - 11:05:09 AM
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Vinh Truong Hoang. Sélection d'attributs multi-espace à partir de motifs binaires locaux pour la classification de textures couleur. Signal and Image Processing. Université du Littoral Côte d'Opale, 2018. English. ⟨NNT : 2018DUNK0468⟩. ⟨tel-01756931⟩

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