Diversifying the image retrieval results

In the area of image retrieval, post-retrieval processing is often used to refine the retrieval results to better satisfy users' requirements. Previous methods mainly focus on presenting users with relevant results. However, in most cases, users cannot clearly present their requirements by several query words. Therefore, relevant results with rich topic coverage are more likely to meet users' ambiguous needs. In this paper, a re-ranking method based on topic richness analysis is proposed to enrich topic coverage in retrieval results. Furthermore, a quantitative criterion called diversity scores (DS) is proposed to evaluate the improvement. Given a set of images, topics that are rarely included in the set are scarce topics, as oppose to rich topics that are widely distributed among the set. Scarce topics contribute more than rich topics do to the DS of images. Five researchers are invited to evaluate the re-ranked results both in topic coverage and relevance. Experimental results on over 20,000 images demonstrate that our proposed approach is effective in improving the topic coverage of retrieval results without loss of relevance.EI