Recursive templates segmentation and exemplars matching for human parsing

Most previous studies need to learn a complex object model for parsing a specific object instance. This paper directly learns the general parsing patterns from the set of parsed objects and formalizes the parsing patterns as a series of parsing templates instead of learning the complex object model. Moreover, a novel hierarchical structure is presented to represent an object by using the parsing templates, which implicitly contains the multi-scale object parts and their relationships. For a single object, the parsing process is equivalent to establishing its hierarchical representation and determining the parsing template for each node. We combine the top-down decomposing scheme and the bottom-up composing scheme to infer the parsing process and formalize the inference as an energy minimization problem. The effect of our method is demonstrated by parsing the human body with aggressive pose variations. Compared with the state-of-the-art methods, the parsing results are more satisfying.