Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.
Description
Welcome to IEEE Xplore 2.0: Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
%0 Journal Article
%1 1021881
%A Naphade, M.R.
%A Huang, T.S.
%D 2002
%J Neural Networks, IEEE Transactions on
%K Bayesian algorithm database decision graphs image indexing information knowledge learning machine multimedia networks pattern recognition retrieval semantic statistical sum-product video
%N 4
%P 793-810
%R 10.1109/TNN.2002.1021881
%T Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1021881
%V 13
%X Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.
@article{1021881,
abstract = { Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.},
added-at = {2009-12-22T12:19:13.000+0100},
author = {Naphade, M.R. and Huang, T.S.},
biburl = {https://www.bibsonomy.org/bibtex/28d85e59b6074d5a8afbbae373d72808d/tanjabuttler},
description = {Welcome to IEEE Xplore 2.0: Extracting semantics from audio-visual content: the final frontier in multimedia retrieval},
doi = {10.1109/TNN.2002.1021881},
interhash = {08a1b8900cec8bd01d770dbd3d4d1447},
intrahash = {8d85e59b6074d5a8afbbae373d72808d},
issn = {1045-9227},
journal = {Neural Networks, IEEE Transactions on},
keywords = {Bayesian algorithm database decision graphs image indexing information knowledge learning machine multimedia networks pattern recognition retrieval semantic statistical sum-product video},
month = Jul,
number = 4,
pages = { 793-810},
timestamp = {2009-12-22T12:19:13.000+0100},
title = {Extracting semantics from audio-visual content: the final frontier in multimedia retrieval},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1021881},
volume = 13,
year = 2002
}