Exploring consistent preferences: discrete hashing with pair-exemplar for scalable landmark search

Lei Zhu, Zi Huang, Xiaojun Chang, Jingkuan Song, Heng Tao Shen

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

39 Citations (Scopus)


Content-based visual landmark search (CBVLS) enjoys great importance in many practical applications. In this paper, we propose a novel discrete hashing with pair-exemplar (DHPE) to support scalable and efficient large-scale CBVLS. Our approach mainly solves two essential problems in scalable landmark hashing: 1) Intralandmark visual diversity, and 2) Discrete optimization of hashing codes. Motivated by the characteristic of landmark, we explore the consistent preferences of tourists on landmark as pair-exemplars for scalable discrete hashing learning. In this paper, a pair-exemplar is comprised of a canonical view and the corresponding representative tags. Canonical view captures the key visual component of landmarks, and representative tags potentially involve landmark-specific semantics that can cope with the visual variations of intra-landmark. Based on pair-exemplars, a unified hashing learning framework is formulated to combine visual preserving with exemplar graph and the semantic guidance from representative tags. Further to guarantee direct semantic transfer for hashing codes and remove information redundancy, we design a novel optimization method based on augmented Lagrange multiplier to explicitly deal with the discrete constraint, the bit uncorrelated constraint and balance constraint. The whole learning process has linear computation complexity and enjoys desirable scalability. Experiments demonstrate the superior performance of DHPE compared with state-of-the-art methods.

Original languageEnglish
Title of host publicationMM'17 - Proceedings of the 2017 ACM Multimedia Conference
Subtitle of host publicationOctober 23-27, 2017 Mountain View, CA, USA
EditorsKuan-Ta Chen, Susanne Boll, Phoebe Chen, Gerald Friedland, Jia Li, Shuicheng Yan
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450349062
Publication statusPublished - 2017
Externally publishedYes
EventACM International Conference on Multimedia 2017 - Mountain View, United States of America
Duration: 23 Oct 201727 Oct 2017
Conference number: 25th


ConferenceACM International Conference on Multimedia 2017
Abbreviated titleMM 2017
Country/TerritoryUnited States of America
CityMountain View
Internet address


  • Discrete hashing
  • Landmark search
  • Pair-exemplar

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