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Proceedings Paper

Unsupervised texture feature classification based on cuckoo search and relief algorithm
Author(s): Mingwei Wang; Youchuan Wan; Zhiwei Ye; Maolin Chen
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Paper Abstract

Gabor filters and K-means algorithm are two commonly used texture analysis methods. However, the texture feature vector has a high dimension by using Gabor filters, which will influence the operating efficiency. Meanwhile, K-means algorithm is affected by the initial clustering centers, and it may lead to the decrease of classification accuracy. Hence, Relief algorithm is applied to make a feature selection for Gabor texture feature, and obtain a suitable texture feature sunset. Furthermore, cuckoo search is used to optimize the clustering center of K-means algorithm, and enhance the accuracy and efficiency of texture recognition. Experimental results demonstrate the effectiveness of the proposed method.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201G (21 July 2017); doi: 10.1117/12.2281563
Show Author Affiliations
Mingwei Wang, Wuhan Univ. (China)
Youchuan Wan, Wuhan Univ. (China)
Zhiwei Ye, Hubei Univ. of Technology (China)
Maolin Chen, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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