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

文献类型： 外文期刊

第一作者： Li, Jianyuan

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

作者机构：

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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