Feature subset selection for multi-class SVM based image classification

Multi-class image classification can benefit much from feature

subset selection. This paper extends an error bound of binary SVMs

to a feature subset selection criterion for the multi-class SVMs. By minimizing

this criterion, the scale factors assigned to each feature in a

kernel function are optimized to identify the important features. This

minimization problem can be efficiently solved by gradient-based search

techniques, even if hundreds of features are involved. Also, considering

that image classification is often a small sample problem, the regularization

issue is investigated for this criterion, showing its robustness in this

situation. Experimental study on multiple benchmark image data sets

demonstrates the effectiveness of the proposed approach.