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The Visual Computer

, Volume 29, Issue 6–8, pp 565–575 | Cite as

Accurate and efficient cross-domain visual matching leveraging multiple feature representations

  • Gang Sun
  • Shuhui Wang
  • Xuehui Liu
  • Qingming Huang
  • Yanyun Chen
  • Enhua Wu
Original Article

Abstract

Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency.

Keywords

Visual matching Cross-domain Multiple features Hyperplane hashing 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. This research is supported by National Fundamental Research Grant 973 Program (2009CB320802), NSFC grant (61272326, 61025011), and Research Grant of University of Macau. Image credits: Andy Carvin, Bob Pejman, Leonid Afremov, Mariko Jesse, Risto-Jussi Isopahkala, Steven Allen, www.turbosquid.com.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gang Sun
    • 1
    • 2
  • Shuhui Wang
    • 3
  • Xuehui Liu
    • 1
  • Qingming Huang
    • 2
    • 3
  • Yanyun Chen
    • 1
  • Enhua Wu
    • 1
    • 4
  1. 1.State Key Laboratory of Computer Science, Institute of SoftwareChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Intelligent Information Processing (CAS), Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  4. 4.University of MacauMacaoChina

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