Feature Selection for Image Classification Based on a New Ranking Criterion

Xuan Zhou, Jiajun Wang

School of Electronic and Information Engineering, Soochow University, Suzhou, China.

DOI: 10.4236/jcc.2015.33013                

Abstract

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.

Keywords

Feature Selection for Image Classification Based on a New Ranking Criterion

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Guyon, I. and Elisseeff, A. (2003) An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research, 3, 1157-1182.
[2]	Song, D.J. and Tao, D.C. (2010) Biologically Inspired Feature Manifold for Scene Classification, IEEE Transaction on Image Processing, 19, 174-184.
[3]	Marko, R.S. and Lgor, K. (2003) Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning, 53, 23-69.
[4]	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
[5]	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
[6]	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
[7]	John, G.H., Kohavi, R., Pfleger, K. (1994) Irrelevant Features and the Subset Selection Problem. ICML, 94, 121-129.
[8]	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.
[9]	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
[10]	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
[11]	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
[12]	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