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2013 (Vol. 5, Issue: 03)
Article Information:

Learning to Classify Texture Objects by Particle Swarm Optimization Algorithm

Ye Zhiwei, Chen Hongwei, Liu Wei, Wang Chunzhi and Lai Xudong
Corresponding Author:  Ye Zhiwei 

Key words:  Image segmentation, particle swarm optimization algorithm, texture classification, tuned filter, , ,
Vol. 5 , (03): 990-995
Submitted Accepted Published
June 23, 2012 July 28, 2012 January 21, 2013

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, most of these methods are object to variant rotation and changing scale of the images. Hence, this study presents a novel approach for texture analysis. The approach applies the Particle Swarm Optimization Algorithm in learning the texture filters for texture classifications. In this approach, the texture filter is regarded as the particle; the population of particle is iteratively evaluated according to a statistical performance index corresponding to object classification ability and evolves into the optimal filter using the evolution principles of Particle Swarm Optimization Algorithm. The method has been validated on aerial images and results indicate that proposed method is feasible for texture analysis.
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  Cite this Reference:
Ye Zhiwei, Chen Hongwei, Liu Wei, Wang Chunzhi and Lai Xudong, 2013. Learning to Classify Texture Objects by Particle Swarm Optimization Algorithm.  Research Journal of Applied Sciences, Engineering and Technology, 5(03): 990-995.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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