Dual-ranking for web image retrieval

General image retrieval systems exploit text and link structure to "understand" the content of the web images and lack the discriminative power to deliver visually diverse search results. The result list often contains hundreds of pages, most of which may not be visited, costing a lot of time and energy of users. Unfortunately, many high quality images, containing more visual and semantic information, may appear at these back pages. To tackle this problem, we introduce a re-ranking method called Dual-Rank to improve web image retrieval by clustering and reordering the images retrieved from an image search engine. We first utilize multipartite graph model to represent images and features, then formulate clustering as a constrained multi-objective optimization problem, which can be efficiently solved by semi-definite programming (SDP). The framework of Dual-Rank is composed of Inter-cluster Rank and Intra-cluster Rank, and could rank clusters and images respectively. Our method is evaluated against a standard search engine and significant improvements are reported in terms of MAP, D@n and user experience.