Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
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.