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Proceedings Paper

Automatically enumerating image data clusters using pixel co-density
Author(s): Ryan A. Mercovich
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Paper Abstract

Typical automatic clustering methods struggle to determine the correct number of clusters to properly characterize the data. To estimate the number of clusters in a spectral image data cloud explicitly from the data structure, the pairwise relationships between pixels in the n-dimensional spectral space are exploited. By plotting the average ith co-density between pixels and neighbors, a monotonically increasing function will emerge that characterizes the clusters in the data. Large upward steps in the average neighbor distance function represent the well-grouped clusters in the data. This process can accurately identify the number of clusters in a wide variety of image data automatically.

Paper Details

Date Published: 24 May 2012
PDF: 15 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901S (24 May 2012); doi: 10.1117/12.919039
Show Author Affiliations
Ryan A. Mercovich, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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