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Unsupervised Cross-Modal Hashing with Soft Constraint

  • Yuxuan ZhouEmail author
  • Yaoxian Li
  • Rui Liu
  • Lingyun Hao
  • Yuanliang Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10736)

Abstract

The booming demands for cross-modal retrieval tasks can often bring in its wake the development of retrieval technologies, as people turn to pursuing a more effective way to improve the performance of search results in both accuracy and efficiency, for example by unsupervised cross-modal hashing. It’s worth noting that most of the cross-modal hashing methods focus on utilizing merely one approach to generate hash codes. However, each approach has its own intrinsic drawback, which would inevitably diminish the quality of hash codes. In this paper, we propose a state-of-the-art model named Soft Constraint Hashing (SCH), using a special soft constraint term defined as an “information tunnel” to achieve the goal that conveys information from one approach to another. In particular, this “tunnel” can eliminate potential data noises to some extent and bridge the gap between two unsupervised discrete hashing allocation approaches to simultaneously reinforce the quality of hash codes. The empirical results on publicly available datasets illustrate that our proposed model outperforms all the existing unsupervised cross-model hashing methods.

Keywords

Unsupervised hashing Multi-modal data Soft constraint 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yuxuan Zhou
    • 1
    Email author
  • Yaoxian Li
    • 2
  • Rui Liu
    • 1
  • Lingyun Hao
    • 3
  • Yuanliang Sun
    • 4
  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.State Key Lab Elect Thin Film and Integrated DeviceUniversity of Electronic Science and Technology of ChinaChengduChina
  3. 3.Xidian UniversityXi’anChina
  4. 4.Southeast UniversityNanjingChina

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