Boosting Web image search by co-ranking
To maximally improve the precision among top-ranked images returned by a Web image search engine without putting extra burden on the user, we propose in this paper a novel co-ranking framework which re-ranks the retrieved images to move the irrelevant ones to the tail of the list. The characteristics of the proposed framework can be summarized as follows: (1) making use of the decisions from multi-view of images to boost retrieval performance; (2) generalizing present multi-view algorithms which need labeled data for initialization to the unsupervised case so that no extra interaction is required. To implement the framework, we use one-class support vector machines to train the basic learner, and propose different schemes for combination. Experimental results demonstrate the effectiveness of the proposed framework.