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Recursive estimation of generative models of video
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
In this paper we present a generative model and learning procedure for unsupervised video clustering into scenes. The work addresses two important problems: realistic modeling of the sources of variability in the video and fast transformation invariant frame clustering. We suggest a solution to the problem of computationally intensive learning in this model by combining the recursive model estimation, fast inference, and on-line learning. Thus, we achieve real time frame clustering performance.
doi:10.1109/cvpr.2006.248
dblp:conf/cvpr/PetrovicIJ06
fatcat:3bovu5lyjreo3bcgxdixvbnjmi