ICIP 2006, Atlanta, GA

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Atlanta Conv. & Vis. Bureau


Technical Program

Paper Detail

Session:Semantic Indexing and Retrieval of Images
Time:Wednesday, October 11, 15:00 - 15:20
Presentation: Lecture
Authors: Xun Yuan; University of Science & Technology of China 
 Wei Lai; Microsoft Research Asia 
 Tao Mei; University of Science & Technology of China 
 Xian-Sheng Hua; Microsoft Research Asia 
 Xiu-Qing Wu; University of Science & Technology of China 
 Shipeng Li; Microsoft Research Asia 
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.