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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/45171
- Comparison analysis on supervised learning based solutions for sports video categorization
Jin, Jesse S.
- The University of Newcastle. Faculty of Science & Information Technology, School of Design, Communication and Information Technology
- 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 10th Workshop on Multimedia Signal Processing. Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing (Cairns, Qld 8-10 October, 2008) p. 526-529
- Publisher Link
- Institute of Electrical and Electronics Engineers (IEEE)
learning (artificial intelligence);
principal component analysis;
support vector machines;
video signal processing
- Resource Type
- conference paper
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