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	Nonparametric Density Estimation on A Graph: Learning Framework, Fast Approximation and Application in Image Segmentation
	Zhiding Yu; Oscar C. Au; Ketan Tang; Chunjing Xu
	2011
会议名称	IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
会议地点	Colorado Springs, CO
英文摘要	We present a novel framework for tree-structure embedded density estimation and its fast approximation for mode seeking. The proposed method could find diverse applications in. computer vision and feature space analysis. Given any undirected, connected and weighted graph, the density function is defined as ajoint representation of the feature space and the distance domain on the graph's spanning tree. Since the distance domain of a tree is a constrained one, mode seeking can not be directly achieved by traditional mean shift in both domain, we address this problem by introducing node shifting with force competition and its fast approximation. Our work is closely related to the previous literature of nonparametric methods. One shall see, however; that the new formulation of this problem can lead to many advantages and new characteristics in its application, as will he illustrated later in this paper.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/3260]
专题	深圳先进技术研究院_集成所
作者单位	2011
推荐引用方式 GB/T 7714	Zhiding Yu,Oscar C. Au,Ketan Tang,et al. Nonparametric Density Estimation on A Graph: Learning Framework, Fast Approximation and Application in Image Segmentation[C]. 见:IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Colorado Springs, CO.

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