Several formulations based on Random Fields (RFs) have been proposed for joint categorization and segmentation (JCaS) of objects in images. The RF's sites correspond to pixels or superpixels of an image and one defines potential functions (typically over local neighborhoods) which define costs for the different possible assignments of labels to several different sites. Since the segmentation is unknown a priori, one cannot define potential functions over arbitrarily large neighborhoods as that may cross object boundaries. Categorization algorithms extract a set of interest points from the entire image and solve the categorization problem by optimizing cost functions that depend on the feature descriptors extracted from these interest points. There is some disconnect between segmentation algorithms which consider local neighborhoods and categorization algorithms which consider non-local neighborhoods. In this work, we propose to bridge this gap by introducing a novel formulation which uses models of objects with deformable parts, classically used for object categorization, to solve the JCaS problem. We use these models to introduce two new classes of potential functions for JCaS; (a) the first class of potential functions encodes the model score for detecting an object as a function of its visible parts only, and (b) the second class of potential functions encodes shape priors for each visible part and is used to bias the segmentation of the pixels in the support region of the part, towards the foreground object label. We show that most existing deformable parts formulations can be used to define these potential functions and that the resulting potential functions can be optimized exactly using min-cut. As a result, these new potential functions can be integrated with most existing RF-based formulations for JCaS.
展开▼
