Automatic Video Genre Categorization using Hierarchical SVM
Xun Yuan
Wei Lai
Tao Mei
Xian-Sheng Hua
Xiu-Qing Wu
Shipeng Li
Dept. of EEIS, Univ. of Sci. & Technol. of China, Hefei;
This paper appears in: Image Processing, 2006 IEEE International Conference on
Publication Date: 8-11 Oct. 2006
On page(s): 2905-2908
Location: Atlanta, GA,
ISSN: 1522-4880
ISBN: 1-4244-0480-0
INSPEC Accession Number: 9461957
Digital Object Identifier: 10.1109/ICIP.2006.313037
Current Version Published: 2007-02-20
Abstract
This paper presents an automatic video genre categorization scheme based on the hierarchical ontology on video genres. Ten computable spatio-temporal features are extracted to distinguish the different genres using a hierarchical support vector machines (SVM) classifier built by cross-validation, which consists of a series of SVM classifiers united in a binary-tree form. As the order and genre partition strategy of the SVM classifier series affect the over performance of the united classifier, two optimal SVM binary trees, local and global, are constructed aiming at finding the best categorization orders, i.e., the best tree structure, of the genre ontology. Experimental results show that the proposed scheme outperforms C4.5 decision tree, typical 1-vs-1 SVM scheme, as well as the hierarchical SVM built by K-means
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