Image clustering, an important technology for image processing, has been actively researched for a long period of time. Especially in recent years, with the explosive growth of the Web, image clustering has even been a critical technology to help users digest the large amount of online visual information. However, as far as we know, many previous works on image clustering only used either low-level visual features or surrounding texts, but rarely exploited these two kinds of information in the same framework. To tackle this problem, we proposed a novel method named consistent bipartite graph co-partitioning in this paper, which can cluster Web images based on the consistent fusion of the information contained in both low-level features and surrounding texts. In particular, we formulated it as a constrained multiobjective optimization problem, which can be efficiently solved by semi-definite programming (SDP). Experiments on a realworld Web image collection showed that our proposed method outperformed the methods only based on low-level features or surround texts.
Gao REF proposed tripartite graph model to represent the relations among low-level features, images and surrounding texts.
Web image clustering by consistent utilization of visual features and surrounding texts
MULTIMEDIA '05
Computer Science