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Robust and Index-Compatible Deep Hashing for Accurate and Fast Image Retrieval

  • Jing Liu
  • Dayan Wu
  • Wanqian Zhang
  • Bo LiEmail author
  • Weiping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11165)

Abstract

Hashing methods have been widely used in large-scale image retrieval. However, the constraints on the hash codes of similar images learned by the previous hashing methods are too strong, which may lead to overfitting and difficult convergence. Besides, the binary codes output by the previous hashing methods are not optimally compatible with the multi-index approach, which is the most effective method for Hamming distance query acceleration. In this paper, we propose a novel Robust and Index-Compatible Deep Hashing (RICH) method to learn compact similarity-preserving binary codes, which focuses on improving the retrieval accuracy and time efficiency simultaneously. With the learned binary codes, we can achieve better results compared with the state-of-the-arts in retrieval accuracy. Meanwhile, remarkable promotions of the retrieval time efficiency have been made in the Hamming distance query process.

Keywords

Deep hashing Image retrieval Hamming distance query The multi-index approach 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jing Liu
    • 1
    • 2
  • Dayan Wu
    • 1
    • 2
  • Wanqian Zhang
    • 1
    • 2
  • Bo Li
    • 2
    Email author
  • Weiping Wang
    • 2
  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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