Abstract
In this paper, we describe a statistical video representation and modeling scheme. Video representation schemes are needed to segment a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology, unsupervised clustering via Gaussian mixture modeling extracts coherent space-time regions in feature space, and corresponding coherent segments (video-regions) in the video content. A key feature of the system is the analysis of video input as a single entity as opposed to a sequence of separate frames. Space and time are treated uniformly. The probabilistic space-time video representation scheme is extended to a piecewise GMM framework in which a succession of GMMs are extracted for the video sequence, instead of a single global model for the entire sequence. The piecewise GMM framework allows for the analysis of extended video sequences and the description of nonlinear, nonconvex motion patterns. The extracted space-time regions allow for the detection and recognition of video events. Results of segmenting video content into static versus dynamic video regions and video content editing are presented.
Original language | English |
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Pages (from-to) | 384-396 |
Number of pages | 13 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 26 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2004 |
Externally published | Yes |
Bibliographical note
Funding Information:Part of the work was supported by the Israeli Ministry of Science, grant number 05530462.
Keywords
- Detection of events in video
- Gaussian mixture model
- Video representation
- Video segmentation