In this paper, the problem of part detection, description and selection is discussed. This problem is crucial in the learning algorithms of part-based models, but can’t be solved well when some candidate parts are extracted from background. This paper studies this problem and introduces a new algorithm, HCRF-PS (Hidden Conditional Random Fields for Part Selection), for part detection, description, especially selection. Our algorithm is distinguished for its power to optimize multiple kinds of information at the same time, including texture, color, location and part label. Finally, we did some experiments with HCRF-PS algorithm which give good results on both virtual and real data.
['Wenhao Lu', 'Shengjin Wang', 'Xiaoqing Ding']
Part Detection, Description and Selection Based on Hidden Conditional Random Fields