RO-SVM: Support Vector Machine with Reject Option for Image Categorization
Rong Zhang, D. Metaxas · 2006
When applying Multiple Instance Learning (MIL) for image categorization, an image is treated as a bag containing a number of instances, each representing a region inside the image. The categorization of this image is determined by the labels of these instances, which are not specified in the training data-set. Hence, these instance labels are needed to be estimated together with the classifier. To improve classification reliability, we propose in this paper a new Support Vector Machine approach by incorporating a reject option, named RO-SVM to determine the instance labels, and the rejection region during the training phase simultaneously. Our approach can also be easily extended to solve multi-class classification problems. Experimental results demonstrate that higher categorization accuracy can be achieved with our RO-SVM method, comparing to approaches that do not exclude uninformative image patches. Our method is able to produce results comparable even with few training samples. 1