Automatic partitioning of full-motion video
Multimedia Systems
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
Exploring Video Structure Beyond The Shots
ICMCS '98 Proceedings of the IEEE International Conference on Multimedia Computing and Systems
Automatic Video Scene Extraction by Shot Grouping
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
A General Framework for Temporal Video Scene Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A new graph-theoretic approach to clustering and segmentation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
Video scene segmentation by improved visual shot coherence
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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Video scene segmentation plays an important role in video structure analysis. In this paper, we propose a time constraint dominant-set clustering algorithm for shot grouping and scene segmentation, in which the similarity between shots is based on autocorrelogram feature, motion feature and time constraint. Therefore, the visual evidence and time constraint contained in the video content are effectively incorporated into a unified clustering framework. Moreover, the number of clusters in our algorithm does not need to be predefined and thus it provides an automatic framework for scene segmentation. Compared with normalized cut clustering based scene segmentation, our algorithm can achieve more accurate results and requires less computing resources.