  Supervoxel segmentation has strong potential to be incorporated into early
video analysis as superpixel segmentation has in image analysis. However, there
are many plausible supervoxel methods and little understanding as to when and
where each is most appropriate. Indeed, we are not aware of a single
comparative study on supervoxel segmentation. To that end, we study seven
supervoxel algorithms, including both off-line and streaming methods, in the
context of what we consider to be a good supervoxel: namely, spatiotemporal
uniformity, object/region boundary detection, region compression and parsimony.
For the evaluation we propose a comprehensive suite of seven quality metrics to
measure these desirable supervoxel characteristics. In addition, we evaluate
the methods in a supervoxel classification task as a proxy for subsequent
high-level uses of the supervoxels in video analysis. We use six existing
benchmark video datasets with a variety of content-types and dense human
annotations. Our findings have led us to conclusive evidence that the
hierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) and
temporal superpixels (TSP) methods are the top-performers among the seven
methods. They all perform well in terms of segmentation accuracy, but vary in
regard to the other desiderata: GBH captures object boundaries best; SWA has
the best potential for region compression; and TSP achieves the best
undersegmentation error.
