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Proceedings Paper

Comparison of three clustering algorithms and an application to color image compression
Author(s): Jihun Cha; Laurene V. Fausett
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Paper Abstract

This paper investigates a traditional clustering algorithm (K-means) and two neural networks (SOM and ART-F). The characteristics of each algorithm are illustrated by simulating geometric space data clustering. Then each algorithm is applied to image data sets to compress the size by reducing the number of colors from 256 to 16.

Paper Details

Date Published: 4 April 1997
PDF: 11 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271482
Show Author Affiliations
Jihun Cha, Florida Institute of Technology (United States)
Laurene V. Fausett, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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