In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to evaluate the final image classification accuracy. Experimental results show that our proposed relief- SVM-RFE algorithm can achieve significant improvements for feature selection in image classification.
 Zhang, Y., Ding, C. and Li, T. (2008) Gene Selection Algorithm by Combining ReliefF and mRMR. BMC Genomics, 9, S27. http://dx.doi.org/10.1186/1471-2164-9-S2-S27
 Suykens, J.A.K. and Vandewalle, J. (1999) Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 9, 293-300. http://dx.doi.org/10.1023/A:1018628609742
 Guyon, I., Weston, J., Barnhill, S., et al. (2002) Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning, 46, 389-422. http://dx.doi.org/10.1023/A:1012487302797
 Yang, J., Yu, K., Gong, Y., et al. (2009) Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, 1794-1801.
 Van De Sande, K.E.A., Gevers, T. and Snoek, C.G.M. (2010) Evaluating Color Descriptors for Object and Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1582-1596. http://dx.doi.org/10.1109/TPAMI.2009.154
 Chun, Y.D., Kim, N.C. and Jang, I.H. (2008) Content-Based Image Retrieval Using Multiresolution Color and Texture Features. IEEE Transactions on Multimedia, 10, 1073-1084. http://dx.doi.org/10.1109/TMM.2008.2001357
 Carlin, M. (2001) Measuring the Performance of Shape Similarity Retrieval Methods. Computer Vision and Image Understanding, 84, 44-61. http://dx.doi.org/10.1006/cviu.2001.0935
 Mundra, P.A. and Rajapakse, J.C. (2010) SVM-RFE with MRMR Filter for Gene Selection. IEEE Transactions on NanoBioscience, 9, 31-37. http://dx.doi.org/10.1109/TNB.2009.2035284