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Proceedings Paper

Vector quantization: a tool for exploration and analysis of multivariate images
Author(s): David A. Southard
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Paper Abstract

We discuss how vector quantization, a technique well known for data compression, can be applied to exploratory data visualization. This technique is especially useful for multivariate imagery, because it reduces the data to a manageable size, without stripping important features. Previous visualization methods are able to combine up to three variables per pixel into an integrated display. Our vector quantization technique allows us to integrate essentially any number of variables per pixel. Furthermore, the cluster analysis inherent in vector quantization has the property of identifying relationships within the data, based on similarity of textural and sample features. We use straightforward techniques to visualize these relationships interactively. The result is a tool that applies to a wide variety of imagery visualization problems. Our prototype uses contrast enhancement, color scales, and highlighting for interactive feature extraction. We show examples from panchromatic and multispectral earth observation satellites and medical imagery.

Paper Details

Date Published: 7 April 1995
PDF: 11 pages
Proc. SPIE 2410, Visual Data Exploration and Analysis II, (7 April 1995); doi: 10.1117/12.205983
Show Author Affiliations
David A. Southard, Univ. of Massachusetts/Lowell (United States)
MITRE Corp. (United States)

Published in SPIE Proceedings Vol. 2410:
Visual Data Exploration and Analysis II
Richard N. Ellson; Georges G. Grinstein; Robert F. Erbacher, Editor(s)

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