Optimal Image and Video Closure by Superpixel Grouping

Detecting independent objects in images and videos is an important perceptual grouping problem. One common perceptual grouping cue that can facilitate this objective is the cue of contour closure, reflecting the spatial coherence of objects in the world and their projections as closed boundaries separating figure from background. Detecting contour closure in images consists of finding a cycle of disconnected contour fragments that separates an object from its background. Searching the entire space of possible groupings is intractable, and previous approaches have adopted powerful perceptual grouping heuristics, such as proximity and co-curvilinearity, to constrain the search. We introduce a new formulation of the problem, by transforming the problem of finding cycles of contour fragments to finding subsets of superpixels whose collective boundary has strong edge support (few gaps) in the image. Our cost function, a ratio of a boundary gap measure to area, promotes spatially coherent sets of superpixels. Moreover, its properties support a global optimization procedure based on parametric maxflow. Extending closure detection to videos, we introduce the concept of spatiotemporal closure. Analogous to image closure, we formulate our spatiotemporal closure cost over a graph of spatiotemporal superpixels. Our cost function is a ratio of motion and appearance discontinuity measures on the boundary of the selection to an internal homogeneity measure of the selected spatiotemporal volume. The resulting approach automatically recovers coherent components in images and videos, corresponding to objects, object parts, and objects with surrounding context, providing a good set of multiscale hypotheses for high-level scene analysis. We evaluate both our image and video closure frameworks by comparing them to other closure detection approaches, and find that they yield improved performance.