Article

Locality preserving clustering for image database

Authors:

Xueyin LinAuthors Info & Claims

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

Pages 885 - 891

https://doi.org/10.1145/1027527.1027731

Published: 10 October 2004 Publication History

Abstract

It is important and challenging to make the growing image repositories easy to search and browse. Image clustering is a technique that helps in several ways, including image data preprocessing, user interface designing, and search result representation. Spectral clustering method has been one of the most promising clustering methods in the last few years, because it can cluster data with complex structure, and the (near) global optimum is guaranteed. However, existing spectral clustering algorithms, like Normalized Cut, are difficult to handle data points out of training set. In this paper, we propose a clustering algorithm named Locality Preserving Clustering (LPC), which shares many of the data representation properties of nonlinear spectral method. Yet LPC provides an explicit mapping function which is defined everywhere, both on training data points and testing points. Experimental results show that LPC is more accurate than both "direct Kmeans" and "PCA + Kmeans". We also show that LPC produces in general comparable results with Normalized Cut, yet is more efficient than Normalized Cut.

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Cited By

Yang HDeng ZZhang WWu QChoi KWang S(2024)End-to-End Multiview Fuzzy Clustering With Double Representation Learning and Visible-Hidden View CooperationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.330092532:2(483-497)Online publication date: Feb-2024
https://doi.org/10.1109/TFUZZ.2023.3300925
Sun DYang YEvans RShpitser I(2023)Locally regularized sparse graph by fast proximal gradient descentProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3626028(2069-2077)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3626028
Chen J(2023)Survey of Evaluation Methods and Metrics for Face Clustering2023 IEEE 13th International Conference on Electronics Information and Emergency Communication (ICEIEC)10.1109/ICEIEC58029.2023.10201018(181-186)Online publication date: 14-Jul-2023
https://doi.org/10.1109/ICEIEC58029.2023.10201018
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Index Terms

Locality preserving clustering for image database
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
      1. Image processing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

cover image ACM Conferences

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

October 2004

1028 pages

ISBN:1581138938

DOI:10.1145/1027527

General Chairs:
Henning Schulzrinne
Columbia University
,
Nevenka Dimitrova
Philips Research
,
Program Chairs:
Angela Sasse
UCL
,
Sue Moon
KAIST
,
Rainer Lienhart
U Augsburg

Copyright © 2004 ACM.

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Publication History

Published: 10 October 2004

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MM04: 2004 12th Annual ACM International Conference on Multimedia

October 10 - 16, 2004

NY, New York, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Yang HDeng ZZhang WWu QChoi KWang S(2024)End-to-End Multiview Fuzzy Clustering With Double Representation Learning and Visible-Hidden View CooperationIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.330092532:2(483-497)Online publication date: Feb-2024
https://doi.org/10.1109/TFUZZ.2023.3300925
Sun DYang YEvans RShpitser I(2023)Locally regularized sparse graph by fast proximal gradient descentProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3626028(2069-2077)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3626028
Chen J(2023)Survey of Evaluation Methods and Metrics for Face Clustering2023 IEEE 13th International Conference on Electronics Information and Emergency Communication (ICEIEC)10.1109/ICEIEC58029.2023.10201018(181-186)Online publication date: 14-Jul-2023
https://doi.org/10.1109/ICEIEC58029.2023.10201018
Manojlovic TMilanic MStajduhar I(2021)Deep embedded clustering algorithm for clustering PACS repositories2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS52027.2021.00091(401-406)Online publication date: Jun-2021
https://doi.org/10.1109/CBMS52027.2021.00091
Shen DLi XYan G(2020)Improve the spectral clustering by integrating a new modularity similarity index and out-of-sample extensionModern Physics Letters B10.1142/S0217984920501055(2050105)Online publication date: 4-Feb-2020
https://doi.org/10.1142/S0217984920501055
Yang XCai ZLi RZhu W(2020)GDPC: generalized density peaks clustering algorithm based on order similarityInternational Journal of Machine Learning and Cybernetics10.1007/s13042-020-01198-0Online publication date: 20-Sep-2020
https://doi.org/10.1007/s13042-020-01198-0
Agarwal AChattopadhyay PWang L(2020)Privacy preservation through facial de-identification with simultaneous emotion preservationSignal, Image and Video Processing10.1007/s11760-020-01819-9Online publication date: 27-Nov-2020
https://doi.org/10.1007/s11760-020-01819-9
Ali MAsghar MBaloch A(2020)An efficient approach for sub-image separation from large-scale multi-panel images using dynamic programmingMultimedia Tools and Applications10.1007/s11042-020-09950-yOnline publication date: 7-Oct-2020
https://doi.org/10.1007/s11042-020-09950-y
Li RYang XQin XZhu W(2019)Local gap density for clustering high-dimensional data with varying densitiesKnowledge-Based Systems10.1016/j.knosys.2019.104905(104905)Online publication date: Aug-2019
https://doi.org/10.1016/j.knosys.2019.104905
Pech RHao DCheng HZhou T(2019)Enhancing subspace clustering based on dynamic predictionFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7128-713:4(802-812)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11704-018-7128-7
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