Web image clustering by consistent utilization of visual features and surrounding texts

  • Authors:
  • Bin Gao;Tie-Yan Liu;Tao Qin;Xin Zheng;Qian-Sheng Cheng;Wei-Ying Ma

  • Affiliations:
  • Microsoft Research Asia, Beijing, P. R. China and Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China and Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China and Peking University, Beijing, P. R. China;Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China

  • Venue:
  • Proceedings of the 13th annual ACM international conference on Multimedia
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 multi-objective optimization problem, which can be efficiently solved by semi-definite programming (SDP). Experiments on a real-world Web image collection showed that our proposed method outperformed the methods only based on low-level features or surround texts.