Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

Guoqiang Xiao, Yang Jiang, Gang Song

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
2 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)127003
JournalOptical Engineering
Volume49
Issue number12
DOIs
Publication statusPublished - Dec 2010

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