Abstract Traditional feature weighting algorithms only weight sample globally for mixed attribute data,which ignores the fact that different feature extraction methods are suited to extract different aspects of texture feature. Therefore,a dual weighted strategy is proposed based on the validities and feature importance. Firstly,the fused features of quaternion wavelet transform and gray level cooccurrence matrix are clustered by the k-means algorithm,and the initial cluster centers are regarded as a reference. The k-nearest neighbor samples extracted from each cluster center are regarded as double weighted training sample sets. Then,the problem of weights inside feature is solved by using modified ReliefF algorithm and correlation measure,and the problem of weights between features is solved by using Support Vector Machine. The experimental results show that the proposed method has a good performance in synthetic textures and natural texture images,and has higher segmentation accuracy than other feature weighting algorithms.
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2014, Vol. 27 
