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Learning to Hash with Binary Deep Neural Network

  • Thanh-Toan Do
  • Anh-Dzung Doan
  • Ngai-Man Cheung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9909)

Abstract

This work proposes deep network models and learning algorithms for unsupervised and supervised binary hashing. Our novel network design constrains one hidden layer to directly output the binary codes. This addresses a challenging issue in some previous works: optimizing non-smooth objective functions due to binarization. Moreover, we incorporate independence and balance properties in the direct and strict forms in the learning. Furthermore, we include similarity preserving property in our objective function. Our resulting optimization with these binary, independence, and balance constraints is difficult to solve. We propose to attack it with alternating optimization and careful relaxation. Experimental results on three benchmark datasets show that our proposed methods compare favorably with the state of the art.

Keywords

Learning to hash Neural network Discrete optimizatization 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Thanh-Toan Do
    • 1
  • Anh-Dzung Doan
    • 1
  • Ngai-Man Cheung
    • 1
  1. 1.Singapore University of Technology and DesignSingaporeSingapore

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