Hierarchical clustering-based navigation of image search results

ABSTRACT

Usually, the image search results contain multiple topics on semantic level and even semantically consistent images have diverse appearances on visual level. How to organize the results into semantically and visually consistent clusters becomes a necessary task to facilitate users' navigation. To attack this, HiCluster, an effective method to organize image search results is designed in this paper, which employs both textual and visual analysis. First, we extract some query-related key phrases to enumerate specific semantics of the given query and cluster them into some semantic clusters using K-lines-based clustering algorithm. Second, the resulting images corresponding to each key phrase are clustered with Bregman Bubble Clustering (BBC) algorithm, which partially groups images in the whole set while discarding some scattered noisy ones. At last, a novel user interface (UI) is designed to provide users with the diverse and helpful information based on the hierarchical clustering structure. Experiments on web images demonstrate the effectiveness and potential of the system.

REFERENCES

• 1: Deng Cai , Xiaofei He , Zhiwei Li , Wei-Ying Ma , Ji-Rong Wen, Hierarchical clustering of WWW image search results using visual, textual and link information, Proceedings of the 12th annual ACM international conference on Multimedia, October 10-16, 2004, New York, NY, USA [doi>10.1145/1027527.1027747]
• 2: Feng Jing , Changhu Wang , Yuhuan Yao , Kefeng Deng , Lei Zhang , Wei-Ying Ma, IGroup: web image search results clustering, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA [doi>10.1145/1180639.1180720]
• 3: Hua-Jun Zeng , Qi-Cai He , Zheng Chen , Wei-Ying Ma , Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009030]
• 4: Igor Fischer. New Method for Spectral Clustering. Technical Report No. IDSIA-12-04 , Hebrew University, Israel, 2004.
• 5: T. Deselaers, D. Keysers, and H. Ney, Clustering visually similar images to improve image search engines, in Informatiktage 2003 der Gesellschaft fur Informatik, 2003.
• 6: Arindam Banerjee , Srujana Merugu , Inderjit S. Dhillon , Joydeep Ghosh, Clustering with Bregman Divergences, The Journal of Machine Learning Research, 6, p.1705-1749, 12/1/2005

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces
          Subjects: Web-based interaction

General Terms:
Algorithms, Design, Experimentation

Keywords:
image search, search result clustering, user interface