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 |
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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 | |
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 |
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Country/Territory | Sweden |
City | Stockholm |
Period | 14/8/24 → 14/8/28 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition