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Optimum Gabor filter design and local binary patterns for texture segmentation

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Li, Ma and Staunton, R. C.. (2008) Optimum Gabor filter design and local binary patterns for texture segmentation. Pattern Recognition Letters, Vol.29 (No.5). pp. 664-672. ISSN 0167-8655

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Official URL: http://dx.doi.org/10.1016/j.patrec.2007.12.001

Abstract

We present a novel approach to multi-texture image segmentation based on the formation of an effective texture feature vector. Texture sub-features are derived from the output of an optimized Gabor filter. The filter's parameters are selected by an immune genetic algorithm, which aims at maximizing the discrimination between the multi-textured regions. Next the texture features are integrated with a local binary pattern, to form an effective texture descriptor with low computational cost, which overcomes the weakness of the single frequency output component of the filter. Finally, a K-nearest neighbor classifier is used to effect the multi-texture segmentation. The integration of the optimum Gabor filter and local binary pattern methods provide a novel solution to the task. Experimental results demonstrate the effectiveness of the proposed approach.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Texture mapping, Gabor transforms, Image processing -- Digital techniques
Journal or Publication Title:	Pattern Recognition Letters
Publisher:	Elsevier BV * North Holland
ISSN:	0167-8655
Date:	1 April 2008
Volume:	Vol.29
Number:	No.5
Number of Pages:	9
Page Range:	pp. 664-672
Identification Number:	10.1016/j.patrec.2007.12.001
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
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URI:	http://wrap.warwick.ac.uk/id/eprint/30320