- Title
- Comparison analysis on supervised learning based solutions for sports video categorization
- Creator
- Xu, M; Park, M; Luo, S; Jin, JS
- Relation
- 2008 IEEE 10th Workshop on Multimedia Signal Processing. Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing (Cairns, QLD 08-10 October, 2008)
- Publisher Link
- http://dx.doi.org/10.1109/MMSP.2008.4665134
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2008
- Description
- Due to the wide viewer-ship and high commercial potentials, recently, sports video analysis attracts extensive research efforts. One of the main tasks in sports video analysis is to identify sports genres i.e. sports video categorization. Most of the existing work focus on mapping content-based features to sports genres by using supervised learning methods. Moreover, video data sets seeks efficient data reduction methods due to the large size and noisy data. It lacks comparison analysis on the implementation and performance of these methods. In this paper, the research is carried out by using four dominant machine learning algorithms, namely Decision Tree, Support Vector Machine, K Nearest Neighbor and Naive Bayesian, and comparing their performance on a high dimensional feature set which selected by some feature selection tools such as Correlation-based Feature Selection (CFS), Principal Components Analysis (PCA) and Relief. Experimental results shows that Support Vector Machine (SVM) and k-NN are not sensitive to reduction of training sets. Moreover, three different feature reduction methods perform very differently with respect to four different tools. © 2008 IEEE.
- Subject
- Bayes methods; decision trees; learning (artificial intelligence); principal component analysis; support vector machines; video signal processing
- Identifier
- http://hdl.handle.net/1959.13/45171
- Identifier
- uon:6051
- Identifier
- ISBN:9781424422951
- Rights
- Copyright © 2008 IEEE. Reprinted from the Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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