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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
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.
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