Hetero-manifold regularisation for cross-modal hashing

Feng Zheng, Yi Tang, Ling Shao

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52 Citations (Scopus)
36 Downloads (Pure)


Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks.
Original languageEnglish
Pages (from-to)1059-1071
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number5
Early online date28 Dec 2016
Publication statusPublished - May 2018


  • Cumulative distance inequality
  • Cross-modal hashing
  • Manifold regularisation
  • Information propagation
  • Hinge loss constraint

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