Scene segmentation by cluster detection in color spaces

Authors:
B. J. Schacter;L. S. Davis;A. Rosenfeld
Affiliations:
University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD
Venue:
ACM SIGART Bulletin
Year:
1976

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Abstract

Ohlander [1] has shown that a variety of scenes can be segmented into meaningful parts by histogramming the values of various point or local properties of the scene; extracting the region whose points gave rise to that peak; and repeating the process for the remainder of the scene. A generalization of this histogram analysis approach is to map the points of the scene into a multi-dimensional feature space, and to look for clusters in this space (a histogram is a mapping into a one-dimensional feature space, in which clusters are peaks). This note illustrates how one of Ohlander's scenes, a house, can be reasonably segmented by mapping it into a three-dimensional color space.