Data Decomposition and Spatial Mixture Modeling for Part based Model

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	Data Decomposition and Spatial Mixture Modeling for Part based Model
	Junge Zhang; Kaiqi Huang; Tieniu Tan
	2012
会议名称	ACCV2012
会议录名称	Springer Berlin Heidelberg, 2012
页码	123-137
会议日期	2012
会议地点	China
摘要	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.
关键词	Data Decomposition
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12692
专题	智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Junge Zhang,Kaiqi Huang,Tieniu Tan. Data Decomposition and Spatial Mixture Modeling for Part based Model[C],2012:123-137.