Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
Dynamic storyboards for video content summarization
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A framework for flexible summarization of racquet sports video using multiple modalities
Computer Vision and Image Understanding
Journal of Visual Communication and Image Representation
An adaptive and efficient unsupervised shot clustering algorithm for sports video
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Multimedia Tools and Applications
Sports Information Retrieval for Video Annotation
International Journal of Digital Library Systems
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We propose a new and efficient approach for clustering dominant scenes in sports video. To perform clustering in an unsupervised manner, we devise a recursive peer-group filtering (PGF) scheme to identify prototypical shots for each dominant scene, and examine time coverage of these prototypical shots to decide the number of dominant scenes for each sports video under analysis. To improve clustering efficiency, we employ principal component analysis and linear discriminant analysis to project high dimensional shot features to lower dimensional spaces suitable for classification. The main contribution of the paper lies in the formulation of clustering dominant scenes in sports video and the development of an efficient, unsupervised solution making use of PGF, time-coverage criterion, and subspace analysis. Experimental results obtained from various types of sports videos are presented to show the efficacy of the proposed approach.