کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1137332 | 1489169 | 2010 | 7 صفحه PDF | دانلود رایگان |
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 large-dataset 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.
Journal: Mathematical and Computer Modelling - Volume 51, Issues 11–12, June 2010, Pages 1332–1338