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

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,