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Probability Ordinal-Preserving Semantic Hashing for Large-Scale Image Retrieval

Published: 21 April 2021 Publication History

Abstract

Semantic hashing enables computation and memory-efficient image retrieval through learning similarity-preserving binary representations. Most existing hashing methods mainly focus on preserving the piecewise class information or pairwise correlations of samples into the learned binary codes while failing to capture the mutual triplet-level ordinal structure in similarity preservation. In this article, we propose a novel Probability Ordinal-preserving Semantic Hashing (POSH) framework, which for the first time defines the ordinal-preserving hashing concept under a non-parametric Bayesian theory. Specifically, we derive the whole learning framework of the ordinal similarity-preserving hashing based on the maximum posteriori estimation, where the probabilistic ordinal similarity preservation, probabilistic quantization function, and probabilistic semantic-preserving function are jointly considered into one unified learning framework. In particular, the proposed triplet-ordering correlation preservation scheme can effectively improve the interpretation of the learned hash codes under an economical anchor-induced asymmetric graph learning model. Moreover, the sparsity-guided selective quantization function is designed to minimize the loss of space transformation, and the regressive semantic function is explored to promote the flexibility of the formulated semantics in hash code learning. The final joint learning objective is formulated to concurrently preserve the ordinal locality of original data and explore potentials of semantics for producing discriminative hash codes. Importantly, an efficient alternating optimization algorithm with the strictly proof convergence guarantee is developed to solve the resulting objective problem. Extensive experiments on several large-scale datasets validate the superiority of the proposed method against state-of-the-art hashing-based retrieval methods.

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Cited By

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  • (2023)Spherical Centralized Quantization for Fast Image RetrievalIEEE Transactions on Image Processing10.1109/TIP.2023.326526232(6485-6499)Online publication date: 1-Jan-2023
  • (2023)A multi-scale multi-level deep descriptor with saliency for image retrievalMultimedia Tools and Applications10.1007/s11042-022-13658-682:24(37939-37958)Online publication date: 1-Oct-2023
  • (2021)Deep Active Context Estimation for Automated COVID-19 DiagnosisACM Transactions on Multimedia Computing, Communications, and Applications10.1145/345712417:3s(1-22)Online publication date: 26-Oct-2021

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Published In

cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 15, Issue 3
June 2021
533 pages
ISSN:1556-4681
EISSN:1556-472X
DOI:10.1145/3454120
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 21 April 2021
Accepted: 01 December 2020
Received: 01 August 2020
Published in TKDD Volume 15, Issue 3

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Author Tags

  1. Learning to hash
  2. image retrieval
  3. ordinal-preserving hashing
  4. discriminative learning

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  • Research-article
  • Refereed

Funding Sources

  • National Natural Science Foundation of China
  • Guangdong Basic and Applied Basic Research Foundation
  • Shenzhen Fundamental Research Fund
  • Key Project of Shenzhen Municipal Technology Research
  • Open Project Fund

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Cited By

View all
  • (2023)Spherical Centralized Quantization for Fast Image RetrievalIEEE Transactions on Image Processing10.1109/TIP.2023.326526232(6485-6499)Online publication date: 1-Jan-2023
  • (2023)A multi-scale multi-level deep descriptor with saliency for image retrievalMultimedia Tools and Applications10.1007/s11042-022-13658-682:24(37939-37958)Online publication date: 1-Oct-2023
  • (2021)Deep Active Context Estimation for Automated COVID-19 DiagnosisACM Transactions on Multimedia Computing, Communications, and Applications10.1145/345712417:3s(1-22)Online publication date: 26-Oct-2021

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