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

Chao Chen, Tao Meng and Lin Lin
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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

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2013, vol. 4, issue 2, 13-45

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.

Date: 2013
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