Time-Constrained Clustering for Segmentation of Video into Story Unites
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A tennis video indexing approach through pattern discovery in interactive process
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part I
IEEE Transactions on Circuits and Systems for Video Technology
Mining repetitive clips through finding continuous paths
Proceedings of the 15th international conference on Multimedia
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There are usually repetitive sub-segments in broadcast videos, which may be associated with high-level concepts or events, e.g., news footage, repeated scores in basketball. Unsupervised mining techniques provide generic solutions to discovering such temporal patterns in various video genres, which are currently the subject of great interests to researchers working on multimedia content analysis. In this paper, we propose a novel approach to automatically detecting repetitive patterns in a video stream. In this approach, a video stream is first transformed to a symbol sequence via the spectral clustering algorithm. After computing the transition probabilities of any two symbols in temporal evolution, we produce a set of probabilistic templates to characterize the patterns of potential interest. Finally, we verify each probabilistic template by measuring the similarities between the video sub-segments and the template. Evaluations on various sports videos show promising results.