Finding Structure in Consumer Videos by Probabilistic Hierarchical Clustering

Accessing, organizing, and manipulating home videos present technical challenges due to their unrestricted content and lack of storyline. In this paper, we present a methodology to discover cluster structure in home videos, which uses video shots as the unit of organization, and is based on two concepts: (i) the development of statistical models of visual similarity and temporal duration and adjacency of consumer video segments, and (ii) the reformulation of hierarchical clustering as a sequential binary Bayesian classification process. A Bayesian formulation allows for the incorporation of prior knowledge of the structure of home video, and offers the advantages of a principled methodology. Gaussian mixture models are used to represent the class-conditional distributions of inter-segment visual similarity, and temporal adjacency and duration. The models are then used in the probabilistic clustering algorithm, where the merging order is a variation of Highest Confidence First, and the merging criterion is Maximum a Posteriori. The algorithm does not need any ad-hoc parameter determination. We present extensive results on a ten-hour home video database with ground-truth which thoroughly validate the performance of our methodology with respect to cluster detection, individual shot-cluster labeling, and the effect of prior selection.

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