Diversifying the Image Relevance Reranking with Absorbing Random Walks

Abstract

Image visual reranking holds the simple search mechanism preferred by typical users, and exploits the visual information and image analysis methods in another way. Therefore, it integrates characteristics of real-time and accuracy, and has great importance to establish practical image search system. A novel reranking method named DIRRA is proposed in this paper, in which absorbing random walks is utilized to enhance the diversity as well as relevance of the initial search results. Four kinds of image visual features are extracted firstly, and then a graph is built, where nodes are images and edges are the similarities between images. Next, the first item is decided by teleporting random walks on the graph, and the other items are decided by absorbing random walks on the graph at last. Experiments are performed on a web image database including 10 queries, which prove the reranking results are both diverse and relevant, and practical to improve user's satisfaction in web search.