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.

A method is described for the color image description by using stable view points of an image obtained after Gaussian smoothing. The basic idea is to determine the view points and corresponding viewing fields at various levels from the color information and define the local image by using these points and fields so that the image matching can be realized. This approach may be developed into a generalized method for image recognition.>