Automatically enumerating image data clusters using pixel co-density

Ryan A. Mercovich

doi:10.1117/12.919039

24 May 2012 Automatically enumerating image data clusters using pixel co-density

Ryan A. Mercovich

Author Affiliations +

Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII; 83901S (2012) https://doi.org/10.1117/12.919039
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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 i^th 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.

Citation Download Citation

Ryan A. Mercovich "Automatically enumerating image data clusters using pixel co-density", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901S (24 May 2012); https://doi.org/10.1117/12.919039

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