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Compact color–texture description for texture classification
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Department of Information and Computer Science, Aalto University School of Science, Finland.
Computer Vision Center, CS Dept. Universitat Autonoma de Barcelona, Spain.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
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2015 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 51, 16-22 p.Article in journal (Refereed) Published
Abstract [en]

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%7.8%,4.3% and 5.0%5.0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 51, 16-22 p.
Keyword [en]
Texture features; Color features; Texture classification; Image classification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-111508DOI: 10.1016/j.patrec.2014.07.020ISI: 000345687500003OAI: oai:DiVA.org:liu-111508DiVA: diva2:756961
Available from: 2014-10-20 Created: 2014-10-20 Last updated: 2017-12-05Bibliographically approved

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Khan, Fahad ShahbazFelsberg, Michael

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Computer VisionThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)
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Electrical Engineering, Electronic Engineering, Information EngineeringComputer Vision and Robotics (Autonomous Systems)

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