Dual Dimensionality Reduction for Efficient Video Similarity Search

  • Zi Huang
  • Heng Tao Shen
  • Xiaofang Zhou
  • Jie Shao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4654)

Abstract

With ever more advanced development in video devices and their extensive usages, searching videos of user interests from large scale repositories, such as web video databases, is gaining its importance. However, the huge complexity of video data, caused by high dimensionality of frames (or feature dimensionality) and large number of frames (or sequence dimensionality), prevents existing content-based search engines from using large video databases. Hence, dimensionality reduction on the data turns out to be most promising. In this paper, we propose a novel video reduction method called Optimal Dual Dimensionality Reduction (ODDR) to dramatically reduce the video data complexity for accurate and quick search, by reducing the dimensionality of both feature vector and sequence. For a video sequence, ODDR first maps each high dimensional frame into a single dimensional value, followed by further reducing the sequence into a low dimensional space. As a result, ODDR approximates each long and high dimensional video sequence into a low dimensional vector. A new similarity function is also proposed to effectively measure the relevance between two video sequences in the reduced space. Our experiments demonstrate the effectiveness of ODDR and its gain on efficiency by several orders of magnitude.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Cheung, S.-C.S., Zakhor, A.: Efficient video similarity measurement with video signature. IEEE Trans. Circuits Syst. Video Techn. 13(1), 59–74 (2003)CrossRefGoogle Scholar
  3. 3.
    Jagadish, H.V., Ooi, B.C., Tan, K.-L., Yu, C., Zhang, R.: idistance: An adaptive b\(^{\mbox{+}}\)-tree based indexing method for nearest neighbor search. ACM Trans. Database Syst. 30(2), 364–397 (2005)CrossRefGoogle Scholar
  4. 4.
    Jain, A.K., Vailaya, A., Xiong, W.: Query by video clip. Multimedia Syst. 7(5), 369–384 (1999)CrossRefGoogle Scholar
  5. 5.
    Keogh, E.J., Chakrabarti, K., Mehrotra, S., Pazzani, M.J.: Locally adaptive dimensionality reduction for indexing large time series databases. In: SIGMOD Conference, pp. 151–162 (2001)Google Scholar
  6. 6.
    Kim, S.H., Park, R.-H.: An efficient algorithm for video sequence matching using the modified hausdorff distance and the directed divergence. IEEE Trans. Circuits Syst. Video Techn. 12(7), 592–596 (2002)CrossRefGoogle Scholar
  7. 7.
    Naphade, M.R., Wang, R., Huang, T.S.: Multimodal pattern matching for audio-visual query and retrieval. In: Storage and Retrieval for Image and Video Databases (SPIE), pp. 188–195 (2001)Google Scholar
  8. 8.
    Shen, H.T., Ooi, B.C., Zhou, X., Huang, Z.: Towards effective indexing for very large video sequence database. In: SIGMOD Conference, pp. 730–741 (2005)Google Scholar
  9. 9.
    Yuan, J., Duan, L.-Y., Tian, Q., Ranganath, S., Xu, C.: Fast and robust short video clip search for copy detection. In: PCM, pp. 479–488 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zi Huang
    • 1
  • Heng Tao Shen
    • 1
  • Xiaofang Zhou
    • 1
  • Jie Shao
    • 1
  1. 1.School of ITEE, The University of QueenslandAustralia

Personalised recommendations