Inference and learning with hierarchical compositional models

Summary form only given: In this work we consider the problem of object parsing, namely detecting an object and its components by composing them from image observations. We build to address the computational complexity of the inference problem. For this we exploit our hierarchical object representation to efficiently compute a coarse solution to the problem, which we then use to guide search at a finer level. Starting from our adaptation of the A* parsing algorithm to the problem of object parsing, we then propose a coarse-to-fine approach that is capable of detecting multiple objects simultaneously. We extend this work to automatically learn a hierarchical model for a category from a set of training images for which only the bounding box is available. Our approach consists in (a) automatically registering a set of training images and constructing an object template (b) recovering object contours (c) finding object parts based on contour affinities and (d) discriminatively learning a parsing cost function.