Predictable Dual-View Hashing

Mohammad Rastegari, Jonghyun Choi, Shobeir Fakhraei, Hal Daumé, Larry S. Davis

Research output: Contribution to conferencePaperpeer-review

56 Citations (Scopus)

Abstract

We propose a Predictable Dual-View Hashing (PDH) algorithm which embeds proximity of data samples in the original spaces. We create a cross-view hamming space with the ability to compare information from previously incomparable domains with a notion of 'predictability'. By performing comparative experimental analysis on two large datasets, PASCAL-Sentence and SUN-Attribute, we demonstrate the superiority of our method to the state-of-the-art dual-view binary code learning algorithms.

Original languageEnglish
Pages2365-2373
Number of pages9
Publication statusPublished - 2013
Event30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States
Duration: 2013 Jun 162013 Jun 21

Conference

Conference30th International Conference on Machine Learning, ICML 2013
Country/TerritoryUnited States
CityAtlanta, GA
Period13/6/1613/6/21

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Sociology and Political Science

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