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.
[1] Liu L,Kuang G Y. Overview of Image Textural Feature Extraction Methods. Journal of Image and Graphics,2009,14(4): 622-635 (in Chinese) (刘 丽,匡纲要.图像纹理特征提取方法综述.中国图象图形学报,2009,14(4): 622-635) [2] Tang H,Maitre H,Boujemaa N,et al. On the Relevance of Linear Discriminative Features. Information Sciences,2010,180(18): 3422-3433 [3] Yang J,Yang J Y,Zhang D,et al. Feature Fusion: Parallel Strategy vs. Serial Strategy. Pattern Recognition,2003,36(6): 1369-1381 [4] Xian Y,,Liu H H,Liao Q P. Weighted Fuzzy C-Means Algorithm Based on Multi-feature for Texture Image Segmentation. Modern Scientific Instruments,2011,(1): 23-27 (in Chinese) (鲜 燚,刘海华,廖秋萍.多特征加权的纹理图像FCM分割算法.现代科学仪器,2011,(1): 23-27) [5] Wang L G,Liu G Y,Mei T C,et al. A Segmentation Algorithm for High Resolution Remote Sensing Texture Based on Spectral and Texture Information Weighting. Acta Optica Sinica,2009,29(11): 3010-3017 (in Chinese) (王雷光,刘国英,梅天灿,等.一种光谱与纹理特征加权的高分辨率遥感纹理分割算法.光学学报,2009,29(11): 3010-3017) [6] Han M,Fan J C. A Single Point Approximation Weighted Fuzzy C-means Clustering Method for Classifying Remote Sensing Images. Journal of Image and Graphics,2009,14(11): 2333-2340 (in Chinese) (韩 敏,范剑超.单点逼近型加权模糊C均值算法的遥感图像聚类应用.中国图象图形学报,2009,14(11): 2333-2340) [7] Li J,Gao X B,Jiao L C. A New Feature Weighted Fuzzy Clustering Algorithm. Acta Electronica Sinica,2006,34(1): 89-92 (in Chinese) (李 洁,高新波,焦李成.基于特征加权的模糊聚类新算法.电子学报,2006,34(1): 89-92) [8] Su X,Wang X Y,Wang Z,et al. An New Fuzzy Clustering Algorithm Based on Entropy Weighting. Journal of Computational Information Systems,2010,6(10): 3319-3326 [9] Liu X F,He B B. Remote Sensing Image Classification Based on Neighbor Sample Density and Membership Weighted FCM Algorithm. Chinese Journal of Scientific Instrument,2011,32(10): 2242-2247 (in Chinese) (刘小芳,何彬彬.近邻样本密度和隶属度加权FCM算法的遥感图像分类方法.仪器仪表学报,2011,32(10): 2242-2247) [10] Bahri M,Ashino R,Vaillancourt R. Two-Dimensional Quaternion Wavelet Transform. Applied Mathematics and Computation,2011,218(1): 10-21 [11] Li C R,Li J P,Yang X C,et al. Gait Recognition Using the Magnitude and Phase of Quaternion Wavelet Transform // Proc of the International Conference on Wavelet Active Media Technology and Information Processing. Chengdu,China,2012: 322-324 [12] Zhou J,Xu Y,Yang X K. Quaternion Wavelet Phase Based Stereo Matching for Uncalibrated Images. Pattern Recognition Letters,2007,28(12): 1509-1522 [13] Soulard R,Carré P. Quaternionic Wavelets for Texture Classification. Pattern Recognition Letters,2011,32(13): 1669-1678 [14] Bayro-Corrochano E. The Theory and Use of the Quaternion Wavelet Transform. Journal of Mathematic Imaging and Vision,2006,24(1): 19-35 [15] Chan W L,Choi H,Baraniuk R G. Coherent Multiscale Image Processing Using Dual-Tree Quaternion Wavelets. IEEE Trans on Image Processing,2008,17(7): 1069-1082 [16] Kononenko I. Estimating Attributes: Analysis and Extensions of RELIEF // Proc of the European Conference on Machine Learning. Catania,Italy,1994: 171-182
2014, Vol. 27 
