Automatic video genre classification using multiple SVM votes

Won Dong Jang; Chulwoo Lee; Jae Young Sim; Chang-Su Kim

doi:10.1109/ICPR.2014.459

Automatic video genre classification using multiple SVM votes

Won Dong Jang, Chulwoo Lee, Jae Young Sim, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

A video genre classification algorithm based on the voting from multiple SVMs is proposed in this work. While conventional genre classifiers use generic baseline features, we employ more specialized features to describe five video genres: animation, commercial, entertainment, drama, and sports. We also present a robust classification algorithm using multiple SVMs, which consider all possible binary grouping of the five genres. Given a query video, each SVM casts a probabilistic vote for each genre. Then, the optimal genre with the maximum votes is selected. Experimental results show that the proposed algorithm provides more accurate classification performance than conventional algorithms.

Original language	English
Title of host publication	Proceedings - International Conference on Pattern Recognition
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2655-2660
Number of pages	6
ISBN (Electronic)	9781479952083
DOIs	https://doi.org/10.1109/ICPR.2014.459
Publication status	Published - 2014 Dec 4
Event	22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden Duration: 2014 Aug 24 → 2014 Aug 28

Publication series

Name	Proceedings - International Conference on Pattern Recognition
ISSN (Print)	1051-4651

Other

Other	22nd International Conference on Pattern Recognition, ICPR 2014
Country/Territory	Sweden
City	Stockholm
Period	14/8/24 → 14/8/28

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.2014.459

Cite this

Jang, W. D., Lee, C., Sim, J. Y., & Kim, C.-S. (2014). Automatic video genre classification using multiple SVM votes. In Proceedings - International Conference on Pattern Recognition (pp. 2655-2660). Article 6977171 (Proceedings - International Conference on Pattern Recognition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2014.459

Automatic video genre classification using multiple SVM votes. / Jang, Won Dong; Lee, Chulwoo; Sim, Jae Young et al.
Proceedings - International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers Inc., 2014. p. 2655-2660 6977171 (Proceedings - International Conference on Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jang, WD, Lee, C, Sim, JY & Kim, C-S 2014, Automatic video genre classification using multiple SVM votes. in Proceedings - International Conference on Pattern Recognition., 6977171, Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers Inc., pp. 2655-2660, 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, 14/8/24. https://doi.org/10.1109/ICPR.2014.459

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abstract = "A video genre classification algorithm based on the voting from multiple SVMs is proposed in this work. While conventional genre classifiers use generic baseline features, we employ more specialized features to describe five video genres: animation, commercial, entertainment, drama, and sports. We also present a robust classification algorithm using multiple SVMs, which consider all possible binary grouping of the five genres. Given a query video, each SVM casts a probabilistic vote for each genre. Then, the optimal genre with the maximum votes is selected. Experimental results show that the proposed algorithm provides more accurate classification performance than conventional algorithms.",

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AB - A video genre classification algorithm based on the voting from multiple SVMs is proposed in this work. While conventional genre classifiers use generic baseline features, we employ more specialized features to describe five video genres: animation, commercial, entertainment, drama, and sports. We also present a robust classification algorithm using multiple SVMs, which consider all possible binary grouping of the five genres. Given a query video, each SVM casts a probabilistic vote for each genre. Then, the optimal genre with the maximum votes is selected. Experimental results show that the proposed algorithm provides more accurate classification performance than conventional algorithms.

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Automatic video genre classification using multiple SVM votes

Abstract

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