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Texture classification and discrimination for region-based image retrieval


Citation

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

Abstract

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.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1016/j.jvcir.2014.10.005
Publisher: Academic Press
Keywords: Region-based image retrieval; Texture feature extraction; Texture classification; Gabor wavelet; Curvelet filters; Polynomials; ImageCLEF; Outex
Depositing User: Nida Hidayati Ghazali
Date Deposited: 27 Feb 2018 01:27
Last Modified: 27 Feb 2018 01:27
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.jvcir.2014.10.005
URI: http://psasir.upm.edu.my/id/eprint/46514
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