Optimizing Ranking Measures for Compact Binary Code Learning

  • Guosheng Lin
  • Chunhua Shen
  • Jianxin Wu
Conference paper

DOI: 10.1007/978-3-319-10578-9_40

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)
Cite this paper as:
Lin G., Shen C., Wu J. (2014) Optimizing Ranking Measures for Compact Binary Code Learning. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, Cham

Abstract

Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest—multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cutting-plane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Guosheng Lin
    • 1
  • Chunhua Shen
    • 1
  • Jianxin Wu
    • 2
  1. 1.University of AdelaideAustralia
  2. 2.Nanjing UniversityChina

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