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Grouping objects in multi-band images using an improved eigenvector-based algorithm

文献类型： 外文期刊

作者： Li, Jianyuan 1 ; Zhou, Jiaogen 1 ; Huang, Wenjiang 1 ; Zhang, Jingcheng 1 ; Yang, Xiaodong 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Tongji Univ, Sch Elect & Informat Engn, Shanghai 201800, Peoples R China

3.ShanXi Normal Univ, Sch Engn, Linfen 041000, Peoples R China

4.Acad Agr Sci, Ctr Informat Technol Agr Shanghai, Shanghai 201106, Peoples R China

关键词： Spectral clustering; Eigenvector; Coarsening algorithm; Random graph

期刊名称：MATHEMATICAL AND COMPUTER MODELLING （ 影响因子：1.366； 五年影响因子：1.602 ）

ISSN： 0895-7177

年卷期： 2010 年 51 卷 11-12 期

页码：

收录情况： SCI

摘要： Spectral clustering algorithms have attracted considerable attention in recent years, However, a problem still exists, These approaches are too slow to scale to large problem sizes, This paper aims at addressing a coarsening algorithm for efficiently grouping larged at a set objects within multi-band images, The coarsening algorithm is based on random graph theory, and it proceeds by combining local homogeneous resolution cells into a set of irregular blocks so the spectral clustering algorithms run efficiently at some coarse level, For multi-band images, we formulate the similarity between pairwise objects as a novel normalized expression and reformulate it in the form of a matrix so that we can implement our algorithm in a few lines using IDL, Finally, we illustrate two examples in agriculture which confirm the effectiveness and efficiency of the proposed algorithm, (C)2009 Elsevier Ltd, All rights reserved,

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