Automatic sub-category partitioning and parts localization for learning a robust object model

• The ideas of sub-category and part-based are used to learn a robust object model.
• A novel object representation is proposed based on the topic model.
• An iterative learning process is presented under the semi-supervision way.

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.

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