Data Decomposition and Spatial Mixture Modeling for Part Based Model

This paper presents a system of data decomposition and spatial mixture modeling for part based models. Recently, many enhanced part based models (with

e.g.,

multiple features, more components or parts) have been proposed. Nevertheless, those enhanced models bring high computation cost together with the risk of over-fitting. To tackle this problem, we propose a data decomposition method for part based models which not only accelerates training and testing process but also improves the performance on average. Besides, the original part based model uses a strict rigid structural model to describe the distribution of each part location. It is not “deformable” enough, especially for those instances with different viewpoints or poses in the same aspect ratio. To address this problem, we present a novel spatial mixture modeling method. The spatial mixture embedded model is then integrated into the proposed data decomposition framework. We evaluate our system on the challenging PASCAL VOC2007 and PASCAL VOC2010 datasets, demonstrating the state-of-the-art performance compared with other related methods in terms of accuracy and efficiency.