Compact color-texture description for texture classification

Fahad Shahbaz Khan, Rao Muhammad Anwer, Joost Van De Weijer, Michael Felsberg, Jorma Laaksonen

    Research output: Contribution to journalArticleResearchpeer-review

    39 Citations (Scopus)

    Abstract

    © 2014 Elsevier B.V. All rights reserved. Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature. However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7.8%,4.3% and 5.0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.
    Original languageEnglish
    Pages (from-to)16-22
    JournalPattern Recognition Letters
    Volume51
    DOIs
    Publication statusPublished - 1 Jan 2015

    Keywords

    • Color features
    • Image classification
    • Texture classification
    • Texture features

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