7 April 1995 Vector quantization: a tool for exploration and analysis of multivariate images
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David A. Southard, "Vector quantization: a tool for exploration and analysis of multivariate images", Proc. SPIE 2410, Visual Data Exploration and Analysis II, (7 April 1995); doi: 10.1117/12.205983; https://doi.org/10.1117/12.205983
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