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A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms

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  • Chao Chen

    (Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA)

  • Tao Meng

    (Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA)

  • Lin Lin

    (Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA)

Abstract

The popularity of research on intelligent multimedia services and applications is motivated by the high demand of the convenient access and distribution of pervasive multimedia data. Facing with abundant multimedia resources but inefficient and rather old-fashioned keyword-based retrieval approaches, Intelligent Multimedia Systems (IMS) demand on (i) effective filtering algorithms for storage saving, computation reduction, and dynamic media delivery; and (ii) advanced learning methods to accurately identify target concepts, effectively search personalized media content, and enable media-type driven applications. Nowadays, the web based multimedia applications become more and more popular. Therefore, how to utilize the web technology into multimedia data management and retrieval becomes an important research topic. In this paper, the authors developed a web-based intelligent video retrieval system that integrates effective and efficient MCA-based filtering and subspace-based learning to facilitate end users to retrieve their desired semantic concepts. A web-based demo shows the effectiveness of the proposed intelligent multimedia system to provide relevant results of target semantic concepts retrieved from TRECVID video collections.

Suggested Citation

  • Chao Chen & Tao Meng & Lin Lin, 2013. "A Web-Based Multimedia Retrieval System with MCA-Based Filtering and Subspace-Based Learning Algorithms," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 4(2), pages 13-45, April.
    • Handle: RePEc:igg:jmdem0:v:4:y:2013:i:2:p:13-45
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    Cited by:

    1. Chao Chen & Mei-Ling Shyu & Shu-Ching Chen, 2016. "Weighted subspace modeling for semantic concept retrieval using gaussian mixture models," Information Systems Frontiers, Springer, vol. 18(5), pages 877-889, October.

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