This paper introduces a novel topic model for learning a robust object model. In this hierarchical model, the layout topic is used to capture the local relationships among a limited number of parts when the part topic is used to locate the potential part regions. Naturally, an object model is represented as a probability distribution over a set of parts with certain layouts. Rather than a monolithic model, our object model is composed of multiple sub-category models designed to capture the significant variations in appearance and shape of an object category. Given a set of object instances with a bounding box, an iterative learning process is proposed to divide them into several sub-categories and learn the corresponding sub-category models without any supervision. Through an experiment in object detection, the learned object model is examined and the results highlight the advantages of our present method compared with others. 
Automatic sub-category partitioning and parts localization for learning a robust object model