Improving the image retrieval results via topic coverage graph

Abstract

In the area of image retrieval, search engines are tender to retrieve images that are most relevant to the users' queries. Nevertheless, in most cases, queries cannot be represented just by several query words. Therefore, it is necessary to provide relevant retrieval results with broad topic-coverage to meet the users' ambiguous needs. In this paper, a re-ranking method based on topic coverage analysis is proposed to perform the refinement of retrieval results. A graph called Topic Coverage Graph (TCG) is constructed to model the degree of mutual topic coverage among images. Then, Topic Richness Score (TRS), which is calculated based on TCG, is used to measure the importance of each image in improving the topic coverage of image retrieval results. 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. © Springer-Verlag Berlin Heidelberg 2006.