Supervised discriminative hashing for compact binary codes

Viet Anh Nguyen, Jiwen Lu, Minh N. Do

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Binary hashing has been increasingly popular for efficient similarity search in large-scale vision problems. This paper presents a novel Supervised Discriminative Hashing (SDH) method by jointly modeling the global and local manifold structures. Specifically, a family of discriminative hash functions is designed to map data points of the original highdimensional space into nearby compact binary codes while preserving the geometrical similarity and discriminant properties in both global and local neighborhoods. Furthermore, the quantization loss between the original data and the binary codes together with the even binary code distribution are also taken into account in the optimization to generate more efficient and compact binary codes. Experimental results have demonstrated the proposed method outperforms the state-of-the-art.

Original languageEnglish (US)
Title of host publicationMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Electronic)9781450330633
StatePublished - Nov 3 2014
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: Nov 3 2014Nov 7 2014

Publication series

NameMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia


Other2014 ACM Conference on Multimedia, MM 2014
Country/TerritoryUnited States


  • Approximate nearest neighbor search
  • Binary codes
  • Hashing
  • Supervised hashing

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software

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