Aphash: Anchor-Based Probability Hashing for Image Retrieval

Junjie Chen, William K. Cheung, Anran WANG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a novel unsupervised hashing method called Anchor-based Probability Hashing (APHash) to preserve the similarities by exploiting the distribution of data points. In particular, distances are transformed into probabilities in both original and hash code spaces. Our method aims to learn hash codes which minimize the mismatch between probability distributions of these two spaces. To address the high complexity issue, our method randomly selects a set of anchors and constructs asymmetric probability matrices. In this way, APHash can make use of the correlation between anchors and data points to learn hash codes more efficiently. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed APHash method, outperforming state-of-the-art hashing approaches in the application of image retrieval.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1673-1677
Number of pages5
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

Scopus Subject Areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Hashing
  • Image Retrieval
  • Unsupervised Learning

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