Article

Active semi-supervised fuzzy clustering for image database categorization

Authors:

Nizar Grira,

Michel Crucianu,

Nozha BoujemaaAuthors Info & Claims

MIR '05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval

Pages 9 - 16

https://doi.org/10.1145/1101826.1101831

Published: 10 November 2005 Publication History

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Abstract

We consider data clustering problems where a limited amount of high-level semantic information, in the form of pairwise must-link and cannot-link constraints, can be acquired from the user. This form of supervision will guide the categorization of image databases in order to provide overviews that fit better user expectations. We propose here an effective semi-supervised clustering algorithm, Active Fuzzy Constrained Clustering (AFCC), that minimizes a competitive agglomeration-based cost function with fuzzy terms corresponding to pairwise constraints provided by the user. In order to minimize the amount of constraints required, we define an active mechanism for the selection of candidates for constraints. The comparisons performed on a simple benchmark and on a ground truth image database show that with AFCC the results of clustering can be significantly improved with few constraints, making this semi-supervised approach an attractive alternative in the categorization of image databases.

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Cited By

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Tan SChen LWang W(2023)Coded computing for distributed graph-based semi-supervised learningJUSTC10.52396/JUSTC-2022-013353:4(0401)Online publication date: 2023
https://doi.org/10.52396/JUSTC-2022-0133
Deng YYuan YFu HQu A(2022)Query-Augmented Active Metric LearningJournal of the American Statistical Association10.1080/01621459.2021.2019045118:543(1862-1875)Online publication date: 28-Jan-2022
https://doi.org/10.1080/01621459.2021.2019045
Li YZhou WWang H(2020)F-DPC: Fuzzy Neighborhood-Based Density Peak AlgorithmIEEE Access10.1109/ACCESS.2020.30229548(165963-165972)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3022954
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Index Terms

Active semi-supervised fuzzy clustering for image database categorization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

MIR '05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval

November 2005

274 pages

ISBN:1595932445

DOI:10.1145/1101826

General Chairs:
Hongjiang Zhang
Microsoft Research Advanced Technology Center, China
,
John Smith
IBM T. J. Watson Research Center, Hawthorne, NY, USA
,
Qi Tian
University of Texas at San Antonio, San Antonio, TX, USA

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2005

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MM&Sec '05

Sponsor:

MM&Sec '05: Multimedia and Security Workshop 2005

November 10 - 11, 2005

Hilton, Singapore

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Cited By

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Tan SChen LWang W(2023)Coded computing for distributed graph-based semi-supervised learningJUSTC10.52396/JUSTC-2022-013353:4(0401)Online publication date: 2023
https://doi.org/10.52396/JUSTC-2022-0133
Deng YYuan YFu HQu A(2022)Query-Augmented Active Metric LearningJournal of the American Statistical Association10.1080/01621459.2021.2019045118:543(1862-1875)Online publication date: 28-Jan-2022
https://doi.org/10.1080/01621459.2021.2019045
Li YZhou WWang H(2020)F-DPC: Fuzzy Neighborhood-Based Density Peak AlgorithmIEEE Access10.1109/ACCESS.2020.30229548(165963-165972)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3022954
Crawford BKhayyam HMilani AJazar R(2019)Big Data Modeling Approaches for Engineering ApplicationsNonlinear Approaches in Engineering Applications10.1007/978-3-030-18963-1_8(307-365)Online publication date: 7-Aug-2019
https://doi.org/10.1007/978-3-030-18963-1_8
Dai WMujeeb AErdt MSourin A(2018)Towards Automatic Optical Inspection of Soldering Defects2018 International Conference on Cyberworlds (CW)10.1109/CW.2018.00074(375-382)Online publication date: Oct-2018
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Lin KZhou J(2016)Interactive Multi-task Relationship Learning2016 IEEE 16th International Conference on Data Mining (ICDM)10.1109/ICDM.2016.0035(241-250)Online publication date: Dec-2016
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Barr JCament LBowyer KFlynn P(2014)Active Clustering with Ensembles for Social structure extractionIEEE Winter Conference on Applications of Computer Vision10.1109/WACV.2014.6835999(969-976)Online publication date: Mar-2014
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Biswas AJacobs D(2014)Active Image Clustering with Pairwise Constraints from HumansInternational Journal of Computer Vision10.1007/s11263-013-0680-6108:1-2(133-147)Online publication date: 1-May-2014
https://dl.acm.org/doi/10.1007/s11263-013-0680-6
Barioni MRazente HMarcelino ATraina ATraina C(2014)Open issues for partitioning clustering methodsWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11274:3(161-177)Online publication date: 1-May-2014
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Yang Z(2013)Clustering AnalysisApplied Data Mining10.1201/b15027-6(57-99)Online publication date: 12-Jun-2013
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