Article

Content-based image retrieval by clustering

Authors:

Robert KrovetzAuthors Info & Claims

MIR '03: Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval

Pages 193 - 200

https://doi.org/10.1145/973264.973295

Published: 07 November 2003 Publication History

Abstract

In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query in terms of semantics. This is known as the semantic gap. We introduce a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), which tackles the semantic gap problem based on a hypothesis: semantically similar images tend to be clustered in some feature space. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. Experimental results based on a database of about 60, 000 images from COREL demonstrate improved performance.

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Cited By

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Index Terms

Content-based image retrieval by clustering
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

MIR '03: Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval

November 2003

281 pages

ISBN:1581137788

DOI:10.1145/973264

Conference Chairs:
Nicu Sebe
University of Amsterdam
,
Michael S. Lew
LIACS Media Lab
,
Chabanne Djeraba
LIFL, University of Sciences and Technologies of Lille

Copyright © 2003 ACM.

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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2003

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Nassif JTekli JKamradt MNassif JTekli JKamradt M(2024)Digital Images – The Bread and Butter of Computer VisionSynthetic Data10.1007/978-3-031-47560-3_5(89-106)Online publication date: 4-Jan-2024
https://doi.org/10.1007/978-3-031-47560-3_5
Wang JZhang PChu TCao YZhou YWu TWang BHe CLin D(2023)V3Det: Vast Vocabulary Visual Detection Dataset2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01817(19787-19797)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.01817
Likhobaba DFedulov DUstalov D(2023)Clustering Without Knowing How To: Application and EvaluationAdvances in Information Retrieval10.1007/978-3-031-28241-6_24(262-268)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28241-6_24
Tekli J(2022)An overview of cluster-based image search result organization: background, techniques, and ongoing challengesKnowledge and Information Systems10.1007/s10115-021-01650-9Online publication date: 11-Feb-2022
https://doi.org/10.1007/s10115-021-01650-9
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S NGangodkar D(2021)A novel image retrieval technique based on semi supervised clusteringMultimedia Tools and Applications10.1007/s11042-021-11542-3Online publication date: 24-Nov-2021
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Nisha Chandran SGangodkar DMittal A(2017)A semi-supervised probabilistic model for clustering large databases of complex imagesMultimedia Tools and Applications10.1007/s11042-017-4664-376:21(21937-21959)Online publication date: 1-Nov-2017
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