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	Graph Degree Linkage:Agglomerative Clustering on a Directed Graph
	Wei Zhang; Xiaogang Wang; Deli Zhao; Xiaoou Tang
	2012
会议名称	12th European Conference on Computer Vision (ECCV)
会议地点	意大利
英文摘要	This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average outdegree characterizes the local geometry around a sample. Based on such insights, we define the affinity measure of clusters via the product of average indegree and average outdegree. The product-based affinity makes our algorithm robust to noise. The algorithm has three main advantages: good performance, easy implementation, and high computational efficiency. We test the algorithm on two fundamental computer vision problems: image clustering and object matching. Extensive experiments demonstrate that it outperforms the state-of-the-arts in both applications.
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/172644/3790]
专题	深圳先进技术研究院_集成所
作者单位	2012
推荐引用方式 GB/T 7714	Wei Zhang,Xiaogang Wang,Deli Zhao,et al. Graph Degree Linkage:Agglomerative Clustering on a Directed Graph[C]. 见:12th European Conference on Computer Vision (ECCV) . 意大利.

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