Classifying motion picture soundtrack for video indexing

Milind R. Naphade, Roy Wang, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We investigate a method for classification of patterns with temporal support. This method combines the ability of a nonlinear-kernel based classifier (in the form of a support vector machine) to discriminate and the ability of a first order Markov chain to model temporal transitions. We apply this to the task of classifying motion picture soundtrack. Experiments with classification of the soundtrack into speech and non-speech audio patterns reveal improvement in classification performance using this proposed method over HMM-based classification as well as SVM-based classification. Using a normalized margin obtained from the SVM and mapping it to a non-negative confidence measure bounded by 1, we attempt to alter the classification of patterns close to the separating boundary, by using the constraints on the transition between the two classes. Sound track classification with semantic classes can help browse and index a video efficiently.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Multimedia and Expo
PublisherIEEE Computer Society
Pages953-956
Number of pages4
ISBN (Electronic)0769511988
DOIs
StatePublished - Jan 1 2001
Event2001 IEEE International Conference on Multimedia and Expo, ICME 2001 - Tokyo, Japan
Duration: Aug 22 2001Aug 25 2001

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2001 IEEE International Conference on Multimedia and Expo, ICME 2001
Country/TerritoryJapan
CityTokyo
Period8/22/018/25/01

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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