Cross-View Feature Hashing for Image Retrieval

  • Wei WuEmail author
  • Bin Li
  • Ling Chen
  • Chengqi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)


Traditional cross-view information retrieval mainly rests on correlating two sets of features in different views. However, features in different views usually have different physical interpretations. It may be inappropriate to map multiple views of data onto a shared feature space and directly compare them. In this paper, we propose a simple yet effective Cross-View Feature Hashing (CVFH) algorithm via a “partition and match” approach. The feature space for each view is bi-partitioned multiple times using B hash functions and the resulting binary codes for all the views can thus be represented in a compatible B-bit Hamming space. To ensure that hashed feature space is effective for supporting generic machine learning and information retrieval functionalities, the hash functions are learned to satisfy two criteria: (1) the neighbors in the original feature spaces should be also close in the Hamming space; and (2) the binary codes for multiple views of the same sample should be similar in the shared Hamming space. We apply CVFH to cross-view image retrieval. The experimental results show that CVFH can outperform the Canonical Component Analysis (CCA) based cross-view method.


Feature Space Hash Function Binary Code Canonical Correlation Analysis Multiple View 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Centre for Quantum Computation and Intelligent SystemsUniversity of Technology SydneyUltimoAustralia
  2. 2.Machine Learning Research GroupNational ICT AustraliaEveleighAustralia

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