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 (Print)	9781479952083
DOIs	https://doi.org/10.1109/ICPR.2014.459
Publication status	Published - 2014 Jan 1
Event	22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden Duration: 2014 Aug 24 → 2014 Aug 28

Other

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

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.2014.459

Cite this

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, 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

@inproceedings{ee6bf88a95e942878ecb07ab0358e4ee,

title = "Automatic video genre classification using multiple SVM votes",

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.",

author = "Jang, {Won Dong} and Chulwoo Lee and Sim, {Jae Young} and Chang-Su Kim",

year = "2014",

month = jan,

day = "1",

doi = "10.1109/ICPR.2014.459",

language = "English",

isbn = "9781479952083",

pages = "2655--2660",

booktitle = "Proceedings - International Conference on Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "22nd International Conference on Pattern Recognition, ICPR 2014 ; Conference date: 24-08-2014 Through 28-08-2014",

}

TY - GEN

T1 - Automatic video genre classification using multiple SVM votes

AU - Jang, Won Dong

AU - Lee, Chulwoo

AU - Sim, Jae Young

AU - Kim, Chang-Su

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=84919904482&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84919904482&partnerID=8YFLogxK

U2 - 10.1109/ICPR.2014.459

DO - 10.1109/ICPR.2014.459

M3 - Conference contribution

AN - SCOPUS:84919904482

SN - 9781479952083

SP - 2655

EP - 2660

BT - Proceedings - International Conference on Pattern Recognition

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Conference on Pattern Recognition, ICPR 2014

Y2 - 24 August 2014 through 28 August 2014

ER -

Automatic video genre classification using multiple SVM votes

Abstract

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this