The goal of video cosegmentation is to jointly extract the common foreground regions and/or objects from a set of videos. In this paper, we present an approach for video cosegmentation that uses graph-based hierarchical clustering as its basic component. Actually, in this work, video cosegmentation problem is transformed into a graph-based clustering problem in which a cluster represents a set of similar supervox-els belonging to the analyzed videos. Our graph-based Hierarchical Video Cosegmentation method (or HVC) is divided in two main parts: (i) super-voxel generation and (ii) supervoxel correlation. The former explores only intra-video similarities, while the latter seeks to determine relationships between supervoxels belonging to the same video or to distinct videos. Experimental results provide comparison between HVC and other methods from the literature on two well known datasets, showing that HVC is a competitive one. HVC outperforms on average all the compared methods for one dataset; and it was the second best for the other one. Actually, HVC is able to produce good quality results without being too computational expensive, taking less than 50% of the time spent by any other approach.
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