Layered object categorization

In this paper, we propose a novel framework of object categorization, namely layered object categorization, which takes advantage of hierarchical category information and performs object categorization at different levels. The proposed hierarchical structure of object categories is built bottom-up and top-down simultaneously accordingly to cognitive rules. First, part-based models are learnt to evaluate structure similarities at the basic level and objects are divided into basic categories. Then the decision cues for object categorization at different layers are optimally selected. Prior knowledge about inter-category relationships is utilized to infer objects' higher inclusive concept labels, while the most discriminative visual details of each category at the lower specific levels are selected automatically. We evaluate the proposed method with a hierarchical database and show promising results. The layered object categorization provides an efficient way for dynamically adapting the object categorization results to different applications.