Genetic feature selection for texture classification using 2-D non-separable wavelet bases

Jing Wein Wang, Chin Hsing Chen, Jeng Shyang Pan

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)

摘要

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

原文English
頁(從 - 到)1635-1644
頁數10
期刊IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E81-A
發行號8
出版狀態Published - 1998

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 電腦繪圖與電腦輔助設計
  • 電氣與電子工程
  • 應用數學

指紋

深入研究「Genetic feature selection for texture classification using 2-D non-separable wavelet bases」主題。共同形成了獨特的指紋。

引用此