Weighted Co-SVM for Image Retrieval with MVB Strategy

In relevance feedback, active learning is often used to alleviate the burden of labeling by selecting only the most informative data. Traditional data selection strategies often choose the data closest to the current classification boundary to label, which are in fact not informative enough. In this paper, we propose the moving virtual boundary (MVB) strategy, which is proved to be a more effective way for data selection. The co-SVM algorithm is another powerful method used in relevance feedback. Unfortunately, its basic assumption that each view of the data be sufficient is often untenable in image retrieval. We present our weighted co-SVM as an extension of co-SVM by attaching weight to each view, and thus relax the view sufficiency assumption. The experimental results show that the weighted co-SVM algorithm outperforms co-SVM obviously, especially with the help of MVB data selection strategy.