A Sparse Support Vector Machine Approach to Region-Based Image Categorization

Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstra...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Jinbo Bi, Yixin Chen, James Ze Wang
• Keywords: Computer Vision, CVPR 2005, Diverse Density Framework, Image Categorization Problem, Mil Approaches, Mil Problem, Sparse Svm Approximate