Learning region weighting from relevance feedback in image retrieval

The region-based approach to image retrieval has emerged as one of the most active research directions in the past few years. There are two crucial problems in the region-based systems: the weighting of regions and the use of relevance feedback. The former plays an important role in computing the region-based similarity of two images, while the latter can improve the efficiency and effectiveness of any CBIR system, if properly employed. In this paper, we propose Key-Region, a novel region weighting scheme that is based on the user's relevance feedback information. The region weight that coincides with human perception can not only be used in a query session, but also be memorized and accumulated for future queries. Experimental results on a database of about 10,000 general-purposed images show the effectiveness of our weighting scheme.