Scene segmentation by cluster detection in color spaces

Author:

Schacter B. J.1,Davis L. S.1,Rosenfeld A.1

Affiliation:

1. University of Maryland, College Park, MD

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.

Publisher

Association for Computing Machinery (ACM)

Reference4 articles.

1. J. M. Tenenbaum and S. Weyl A region-analysis subsystem for interactive scene analysis ibid. 682--687. J. M. Tenenbaum and S. Weyl A region-analysis subsystem for interactive scene analysis ibid. 682--687.

2. Multidimensional binary search trees used for associative searching

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