Unsupervised texture feature classification based on cuckoo search and relief algorithm

Mingwei Wang; Youchuan Wan; Zhiwei Ye; Maolin Chen

doi:10.1117/12.2281563

21 July 2017 Unsupervised texture feature classification based on cuckoo search and relief algorithm

Mingwei Wang, Youchuan Wan, Zhiwei Ye, Maolin Chen

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104201G (2017) https://doi.org/10.1117/12.2281563
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Mingwei Wang, Youchuan Wan, Zhiwei Ye, and Maolin Chen "Unsupervised texture feature classification based on cuckoo search and relief algorithm", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104201G (21 July 2017); https://doi.org/10.1117/12.2281563

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