Exciting event detection using multi-level multimodal descriptors and data classification

Shu Ching Chen, Min Chen, Chengcui Zhang, Mei Ling Shyu

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

12 Scopus citations

Abstract

Event detection is of great importance in high-level semantic indexing and selective browsing of video clips. However, the use of low-level visual-audio feature descriptors alone generally fails to yield satisfactory results in event identification due to the semantic gap issue. In this paper, we propose an advanced approach for exciting event detection in soccer video with the aid of multi-level descriptors and classification algorithm. Specifically, a set of algorithms are developed for efficient extraction of meaningful mid-level descriptors to bridge the semantic gap and to facilitate the comprehensive video content analysis. The data classification algorithm is then performed upon the combination of multimodal mid-level descriptors and low-level feature descriptors for event detection. The effectiveness and efficiency of the proposed framework are demonstrated over a large collection of soccer video data with different styles produced by different broadcasters.

Original languageEnglish (US)
Title of host publicationISM 2006 - 8th IEEE International Symposium on Multimedia
Pages193-200
Number of pages8
DOIs
StatePublished - Dec 1 2006
EventISM 2006 - 8th IEEE International Symposium on Multimedia - San Diego, CA, United States
Duration: Dec 11 2006Dec 13 2006

Publication series

NameISM 2006 - 8th IEEE International Symposium on Multimedia

Other

OtherISM 2006 - 8th IEEE International Symposium on Multimedia
CountryUnited States
CitySan Diego, CA
Period12/11/0612/13/06

ASJC Scopus subject areas

  • Computer Networks and Communications

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