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Query Expansion for Content-Based Similarity Search Using Local and Global Features

Authors:

Michael E. Houle,

Jichao SunAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13, Issue 3

Article No.: 25, Pages 1 - 23

https://doi.org/10.1145/3063595

Published: 31 May 2017 Publication History

Abstract

This article presents an efficient and totally unsupervised content-based similarity search method for multimedia data objects represented by high-dimensional feature vectors. The assumption is that the similarity measure is applicable to feature vectors of arbitrary length. During the offline process, different sets of features are selected by a generalized version of the Laplacian Score in an unsupervised way for individual data objects in the database. Online retrieval is performed by ranking the query object in the feature spaces of candidate objects. Those candidates for which the query object is ranked highly are selected as the query results. The ranking scheme is incorporated into an automated query expansion framework to further improve the semantic quality of the search result. Extensive experiments were conducted on several datasets to show the capability of the proposed method in boosting effectiveness without losing efficiency.

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Cited By

Johnsen MAumüller M(2024)An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier SearchSimilarity Search and Applications10.1007/978-3-031-75823-2_13(155-169)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-75823-2_13
Rashad MAfifi IAbdelfatah M(2023)RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query ExpansionJournal of Digital Imaging10.1007/s10278-022-00769-736:3(1248-1261)Online publication date: 26-Jan-2023
https://doi.org/10.1007/s10278-022-00769-7
Ahmed AMalebary S(2020)Query Expansion Based on Top-Ranked Images for Content-Based Medical Image RetrievalIEEE Access10.1109/ACCESS.2020.30335048(194541-194550)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3033504
Show More Cited By

Index Terms

Query Expansion for Content-Based Similarity Search Using Local and Global Features
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 13, Issue 3

August 2017

233 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3104033

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2017

Accepted: 01 February 2017

Revised: 01 January 2017

Received: 01 August 2016

Published in TOMM Volume 13, Issue 3

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Kakenhi Kiban (A) Research
Kiban (B) Research
Japan Society for the Promotion of Science (JSPS)
National Science Foundation (NSF)

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Cited By

Johnsen MAumüller M(2024)An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier SearchSimilarity Search and Applications10.1007/978-3-031-75823-2_13(155-169)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-75823-2_13
Rashad MAfifi IAbdelfatah M(2023)RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query ExpansionJournal of Digital Imaging10.1007/s10278-022-00769-736:3(1248-1261)Online publication date: 26-Jan-2023
https://doi.org/10.1007/s10278-022-00769-7
Ahmed AMalebary S(2020)Query Expansion Based on Top-Ranked Images for Content-Based Medical Image RetrievalIEEE Access10.1109/ACCESS.2020.30335048(194541-194550)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3033504
Bouziri ALatiri CGaussier E(2020)LTR-expand: query expansion model based on learning to rank association rulesJournal of Intelligent Information Systems10.1007/s10844-020-00596-8Online publication date: 21-Mar-2020
https://doi.org/10.1007/s10844-020-00596-8
Nabeel Asim MWasim MUsman Ghani Khan MMahmood W(2018)Improved biomedical term selection in pseudo relevance feedbackDatabase10.1093/database/bay0562018:1Online publication date: 2-Jul-2018
https://doi.org/10.1093/database/bay056
von Ritter LHoule MGünnemann S(2018)Intrinsic Degree: An Estimator of the Local Growth Rate in GraphsSimilarity Search and Applications10.1007/978-3-030-02224-2_15(195-208)Online publication date: 7-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-02224-2_15
Houle MSchubert EZimek A(2018)On the Correlation Between Local Intrinsic Dimensionality and OutliernessSimilarity Search and Applications10.1007/978-3-030-02224-2_14(177-191)Online publication date: 7-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-02224-2_14
Zheng ZZheng LYang Y(2017)A Discriminatively Learned CNN Embedding for Person ReidentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/315917114:1(1-20)Online publication date: 13-Dec-2017
https://dl.acm.org/doi/10.1145/3159171

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