Hop: Hierarchical object parsing

In this paper we consider the problem of object parsing, namely detecting an object and its components by compos-ing them from image observations. Apart from object local-ization, this involves the question of combining top-down (model-based) with bottom-up (image-based) information. We use an hierarchical object model, that recursively de-composes an object into simple structures. Our first contri-bution is the formulation of composition rules to build the object structures, while addressing problems such as con-tour fragmentation and missing parts. Our second contri-bution is an efficient inference method for object parsing that addresses the combinatorial complexity of the 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. This rules out a large portion of futile compositions and allows us to parse complex objects in heavily cluttered scenes. 1