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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