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Optical Engineering

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification
Author(s): Guoqiang Xiao; Yang Jiang; Gang Song; Jianmin Jiang
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Paper Abstract

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

Paper Details

Date Published: 1 December 2010
PDF: 6 pages
Opt. Eng. 49(12) 127003 doi: 10.1117/1.3518080
Published in: Optical Engineering Volume 49, Issue 12
Show Author Affiliations
Guoqiang Xiao, Southwest Univ. (China)
Yang Jiang, Univ. of Bradford (United Kingdom)
Gang Song, Southwest Univ. (China)
Jianmin Jiang, Southwest Univ. (United Kingdom)


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