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 proceedingConference 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 languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2655-2660
Number of pages6
ISBN (Electronic)9781479952083
DOIs
Publication statusPublished - 2014 Dec 4
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 2014 Aug 242014 Aug 28

Publication series

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

Other

Other22nd International Conference on Pattern Recognition, ICPR 2014
Country/TerritorySweden
CityStockholm
Period14/8/2414/8/28

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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