Learning Global and Reconfigurable Part-Based Models for Object Detection

This paper presents a method of learning global and reconfigurable part-based models (RPM) for object detection. Recently, deformable part-based model (DPM) is widely used. A DPM consists of a root node and a collection of part nodes, which is learned under the latent SVM formulation by treating part nodes as hidden variables. Although the configuration of parts (i.e., the shapes, sizes and locations of parts) plays a major role in improving performance of object detection, it has not been addressed well in the literature. In this paper, we propose RPM to tackle it. A dictionary of part types is defined by enumerating rectangular shapes of different aspect ratios and sizes given the whole lattice (often at twice resolution of the root node), and each part type has a set of part instances when placed in the lattice. So, the configuration space of parts is quantized by the part types and part instances, and then organized into a hierarchical And-Or directed a cyclic graph (AOG). The AOG consists of three types of nodes: terminal nodes (i.e., part instances), And-nodes (representing decompositions of a part instance into two smaller ones) and Or-nodes (representing alternative ways of decompositions). The globally optimal configuration in the AOG is solved using dynamic programming (DP) where the classification error rates of terminal nodes and And-nodes are used as their figures of merit. In experiments, we test our method on the 20 object categories in the PASCAL VOC2007 dataset and obtain comparable performance with state-of-the-art methods.