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Texture classification and discrimination for region-based image retrieval

In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, suffic...

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Main Authors: Zand, Mohsen, Doraisamy, Shyamala, Abdul Halin, Alfian, Mustaffa, Mas Rina
Format: Article
Published: Elsevier 2015
Online Access:http://psasir.upm.edu.my/id/eprint/35620/
http://www.sciencedirect.com/science/article/pii/S1047320314001643
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spelling my.upm.eprints.356202016-01-18T02:53:52Z http://psasir.upm.edu.my/id/eprint/35620/ Texture classification and discrimination for region-based image retrieval Zand, Mohsen Doraisamy, Shyamala Abdul Halin, Alfian Mustaffa, Mas Rina In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands for effective texture classification. Moreover, shape irregularity can be a problem since Gabor and curvelet transforms can only be applied on the regular shapes. In this paper, we propose an approach that uses both the Gabor wavelet and the curvelet transforms on the transferred regular shapes of the image regions. We also apply a fitting method to encode the sub-bands’ information in the polynomial coefficients to create a texture feature vector with the maximum power of discrimination. Experiments on texture classification task with ImageCLEF and Outex databases demonstrate the effectiveness of the proposed approach. Elsevier 2015-01 Article PeerReviewed Zand, Mohsen and Doraisamy, Shyamala and Abdul Halin, Alfian and Mustaffa, Mas Rina (2015) Texture classification and discrimination for region-based image retrieval. Journal of Visual Communication and Image Representation, 26. pp. 305-316. ISSN 1047-3203; ESSN: 1095-9076 http://www.sciencedirect.com/science/article/pii/S1047320314001643 10.1016/j.jvcir.2014.10.005
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description In RBIR, texture features are crucial in determining the class a region belongs to since they can overcome the limitations of color and shape features. Two robust approaches to model texture features are Gabor and curvelet features. Although both features are close to human visual perception, sufficient information needs to be extracted from their sub-bands for effective texture classification. Moreover, shape irregularity can be a problem since Gabor and curvelet transforms can only be applied on the regular shapes. In this paper, we propose an approach that uses both the Gabor wavelet and the curvelet transforms on the transferred regular shapes of the image regions. We also apply a fitting method to encode the sub-bands’ information in the polynomial coefficients to create a texture feature vector with the maximum power of discrimination. Experiments on texture classification task with ImageCLEF and Outex databases demonstrate the effectiveness of the proposed approach.
format Article
author Zand, Mohsen
Doraisamy, Shyamala
Abdul Halin, Alfian
Mustaffa, Mas Rina
spellingShingle Zand, Mohsen
Doraisamy, Shyamala
Abdul Halin, Alfian
Mustaffa, Mas Rina
Texture classification and discrimination for region-based image retrieval
author_facet Zand, Mohsen
Doraisamy, Shyamala
Abdul Halin, Alfian
Mustaffa, Mas Rina
author_sort Zand, Mohsen
title Texture classification and discrimination for region-based image retrieval
title_short Texture classification and discrimination for region-based image retrieval
title_full Texture classification and discrimination for region-based image retrieval
title_fullStr Texture classification and discrimination for region-based image retrieval
title_full_unstemmed Texture classification and discrimination for region-based image retrieval
title_sort texture classification and discrimination for region-based image retrieval
publisher Elsevier
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/35620/
http://www.sciencedirect.com/science/article/pii/S1047320314001643
_version_ 1643831507823886336
score 13.211869

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