A Study of Parts-Based Object Class Detection Using Complete Graphs

Object detection is one of the key components in modern computer vision systems. While the detection of a specific rigid object under changing viewpoints was considered hard just a few years ago, current research strives to detect and recognize classes of non-rigid, articulated objects. Hampered by the omnipresent confusing information due to clutter and occlusion, the focus has shifted from holistic approaches for object detection to representations of individual object parts linked by structural information, along with richer contextual descriptions of object configurations. Along this line of research, we present a practicable and expandable probabilistic framework for parts-based object class representation, enabling the detection of rigid and articulated object classes in arbitrary views. We investigate learning of this representation from labelled training images and infer globally optimal solutions to the contextual MAP-detection problem, using A*-search with a novel lower-bound as admissible heuristic. An assessment of the inference performance of Belief-Propagation and Tree-Reweighted Belief Propagation is obtained as a by-product. The generality of our approach is demonstrated on four different datasets utilizing domain dependent information cues.