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Affinity relation discovery in image database clustering and content-based retrieval
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Source International Multimedia Conference archive
Proceedings of the 12th annual ACM international conference on Multimedia table of contents
New York, NY, USA
POSTER SESSION: Technical poster session 1: multimedia analysis, processing, and retrieval table of contents
Pages: 372 - 375  
Year of Publication: 2004
ISBN:1-58113-893-8
Authors
Mei-Ling Shyu  University of Miami, Coral Gables, FL
Shu-Ching Chen  Florida International University, Miami, FL
Min Chen  Florida International University, Miami, FL
Chengcui Zhang  University of Alabama at Birmingham, Birmingham, AL
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Rui, Y., et al. Relevance Feedback: A Power Tool for Interactive Content-based Image Retrieval. IEEE Trans. on Circuit and Video Technology, 8, 5 (1998), 644--655.
 
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Saux B. L. et al. Image Database Clustering with SVM based Class Personalization. In IS&T/SPIE Conf. on Storage and Retrieval Methods & Applications for Multimedia, (2004).
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Shyu, M.-L., et al. Stochastic Clustering for Organizing Distributed Information Source. Accepted for publication, IEEE Trans. on Systems, Man and Cybernetics, Part B, (2004).


Collaborative Colleagues:
Mei-Ling Shyu: colleagues
Shu-Ching Chen: colleagues
Min Chen: colleagues
Chengcui Zhang: colleagues