Random Sampling Based SVM for Relevance Feedback Image Retrieval

Abstract

Relevance feedback (RF) schemes based on support vector machine (SVM) have been widely used in content-based image retrieval. However, the performance of SVM based RF is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: (1) SVM classifier is unstable on small size training set; (2) SVM's optimal hyper-plane may be biased when the positive feedback samples are much less than the negative feedback samples; (3) overfitting due to that the feature dimension is much higher than the size of the training set. In this paper, we try to use random sampling techniques to overcome these problems. To address the first two problems, we propose an asymmetric bagging based SVM. For the third problem, we combine the random subspace method (RSM) and SVM for RF. Finally, by integrating bagging and RSM we solve all the three problems and further improve the RF performance.