A study of semantic context detection by using SVM and GMM approaches

Wei Ta Chu*, Wen-Huang Cheng, Ja Ling Wu, Jane Yung Jen Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Semantic-level content analysis is a crucial issue to achieve efficient content retrieval and management. In this paper, we propose a hierarchical approach that models the statistical characteristics of several audio events over a time series to accomplish semantic context detection. Two stages, including audio event and semantic context modeling/testing, are devised to bridge the semantic gap between physical audio features and semantic concepts. HMMs are used to model audio events, and SVMs and GMMs are used to fuse the characteristics of various audio events related to some specific semantic concepts. The experimental results show that the approach is effective in detecting semantic context. The comparison between SVM- and GMM-based approaches is also studied.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Multimedia and Expo (ICME)
Pages1591-1594
Number of pages4
DOIs
StatePublished - 1 Dec 2004
Event2004 IEEE International Conference on Multimedia and Expo (ICME) - Taipei, Taiwan
Duration: 27 Jun 200430 Jun 2004

Publication series

Name2004 IEEE International Conference on Multimedia and Expo (ICME)
Volume3

Conference

Conference2004 IEEE International Conference on Multimedia and Expo (ICME)
CountryTaiwan
CityTaipei
Period27/06/0430/06/04

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