Hierarchical video segmentation using observation scale

Abstract

Hierarchical video segmentation provides region-oriented scale-space, i.e., a set of video segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarserlevels. In this work, the hierarchical video segmentation is transformed into a graph partitioning problem in which each part corresponds to one supervoxel of the video, and we present a new methodology forhierarchical video segmentation which computes a hierarchy of partitions by a reweighting of the original graph using a simple dissimilarity measure in which a not too coarse segmentation can be easily inferred.We also provide an extensive comparative analysis, considering quantitative assessments showing accuracy, ease of use, and temporal coherence of our method. According to the experiments, the hierarchy inferred by our methods produces good quantitative results when applied to video segmentation, and present a low time and space cost.