research-article

Semantic concept extraction from sports video for highlight generation

Authors:

M. H. Kolekar,

S. SenguptaAuthors Info & Claims

MobiMedia '06: Proceedings of the 2nd international conference on Mobile multimedia communications

Article No.: 26, Pages 1 - 6

https://doi.org/10.1145/1374296.1374324

Published: 18 September 2006 Publication History

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Abstract

A multi-layered, hierarchical framework for generic sports event analysis has been proposed in this paper. Each layer uses different features such as short-time audio energy, short-time Zero Crossing Rate, color histogram, fractal dimension, motion, etc and classifiers such as Hidden Markov Model, threshold-based classifier, etc. We demonstrate our approach on sports video. The automated semantic concept extraction is ideally required for application such as highlight generation, indexing and retrieval.

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Cited By

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Liang JJin QHe XYang GXu JLi X(2014)Semantic Concept Annotation of Consumer Videos at Frame-Level Using AudioProceedings of the 15th Pacific-Rim Conference on Advances in Multimedia Information Processing --- PCM 2014 - Volume 887910.1007/978-3-319-13168-9_12(113-122)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/978-3-319-13168-9_12
Kolekar MSengupta S(2010)Semantic concept mining in cricket videos for automated highlight generationMultimedia Tools and Applications10.1007/s11042-009-0337-147:3(545-579)Online publication date: 1-May-2010
https://dl.acm.org/doi/10.1007/s11042-009-0337-1
Lin LShyu M(2009)Mining High-Level Features from Video Using Associations and CorrelationsProceedings of the 2009 IEEE International Conference on Semantic Computing10.1109/ICSC.2009.59(137-144)Online publication date: 14-Sep-2009
https://dl.acm.org/doi/10.1109/ICSC.2009.59

Index Terms

Semantic concept extraction from sports video for highlight generation
1. Information systems
  1. Information retrieval
  2. Information storage systems

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MobiMedia '06: Proceedings of the 2nd international conference on Mobile multimedia communications

September 2006

281 pages

ISBN:1595935177

DOI:10.1145/1374296

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 18 September 2006

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Liang JJin QHe XYang GXu JLi X(2014)Semantic Concept Annotation of Consumer Videos at Frame-Level Using AudioProceedings of the 15th Pacific-Rim Conference on Advances in Multimedia Information Processing --- PCM 2014 - Volume 887910.1007/978-3-319-13168-9_12(113-122)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1007/978-3-319-13168-9_12
Kolekar MSengupta S(2010)Semantic concept mining in cricket videos for automated highlight generationMultimedia Tools and Applications10.1007/s11042-009-0337-147:3(545-579)Online publication date: 1-May-2010
https://dl.acm.org/doi/10.1007/s11042-009-0337-1
Lin LShyu M(2009)Mining High-Level Features from Video Using Associations and CorrelationsProceedings of the 2009 IEEE International Conference on Semantic Computing10.1109/ICSC.2009.59(137-144)Online publication date: 14-Sep-2009
https://dl.acm.org/doi/10.1109/ICSC.2009.59

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Semantic concept mining in cricket videos for automated highlight generation

Sports wizard: sports video browsing based on semantic concepts and game structure

Concept ontology construction for sports video

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