Independent component analysis for understanding multimedia content

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2002

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Independent component analysis of combined text and image data from Web pages has potential for search and retrieval applications by providing more meaningful and context dependent content. It is demonstrated that ICA of combined text and image features has a synergistic effect, i.e., the retrieval classification rates increase if based on multimedia components relative to single media analysis. For this purpose a simple probabilistic supervised classifier which works from unsupervised ICA features is invoked. In addition, we demonstrate the suggested framework for automatic annotation of descriptive key words to images.
Original languageEnglish
Title of host publicationProceedings of IEEE Workshop on Neural Networks for Signal Processing XII, Martigny, Valais, Switzerland, Sept. 4-6
PublisherIEEE Press
Publication date2002
Pages757-766
ISBN (print)0-7803-7616-1
DOIs
StatePublished

Conference

Conference2002 IEEE Workshop on Neural Networks for Signal Processing XII
CountrySwitzerland
CityMatigny
Period04/09/0206/09/02
Internet addresshttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8007

Bibliographical note

Copyright: 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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Keywords

  • ICA, webmining, multimedia signal processing
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