21 July 2017 Unsupervised texture feature classification based on cuckoo search and relief algorithm
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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.
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Mingwei Wang, Mingwei Wang, Youchuan Wan, Youchuan Wan, Zhiwei Ye, Zhiwei Ye, Maolin Chen, 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); doi: 10.1117/12.2281563; https://doi.org/10.1117/12.2281563
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