Multimedia Tools and Applications

, Volume 55, Issue 1, pp 151–178 | Cite as

Towards hierarchical context: unfolding visual community potential for interactive video retrieval

  • Lin Pang
  • Juan Cao
  • Lei Bao
  • Yongdong Zhang
  • Shouxun Lin
Article

Abstract

Community structure as an interesting property of network has attracted wide attention from many research fields. In this paper, we exploit the visual community structure in visual-temporal correlation network and utilize it to improve interactive video retrieval. Firstly, we propose a hierarchical community-based feedback algorithm. By re-ranking the video shots through diffusion processes respectively on the inter-community and intra-community level, the feedback algorithm can make full use of the limited user feedback. Furthermore, since it avoids entire graph computation, the feedback algorithm can make quick responses to user feedback, which is particularly important for the large video collections. Secondly, we propose a community-based visualization interface called VideoMap. By organizing the video shots following the community structure, the VideoMap presents a comprehensive and informative view of the whole dataset to facilitate users’ access. Moreover, the VideoMap can help users to quickly locate the potential relevant regions and make active annotation according to the distribution of labeled samples on the VideoMap. Experiments on TRECVID 2009 search dataset demonstrate the efficiency of the feedback algorithm and the effectiveness of the visualization interface.

Keywords

Interactive video retrieval Visual community Hierarchical community-based feedback Active annotation 

Notes

Acknowledgments

This work was supported by the National Basic Research Program of China (973 Program, 2007CB311100), National Nature Science Foundation of China (60902090), Beijing New Star Project on Science & Technology (2007B071), Co-building Program of Beijing Municipal Education Commission.

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Lin Pang
    • 2
    • 1
  • Juan Cao
    • 1
  • Lei Bao
    • 1
  • Yongdong Zhang
    • 1
  • Shouxun Lin
    • 1
  1. 1.Laboratory of Advanced Computing Research, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of the Chinese Academy of SciencesBeijingChina

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