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Journal of Electronic Imaging

Unsupervised color-image segmentation by multicolor space iterative pixel classification
Author(s): Nicolas Vandenbroucke; Laurent Busin; Ludovic Macaire
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Paper Abstract

We propose a color-image segmentation algorithm by unsupervised classification of pixels. The originality of the proposed approach consists in iteratively identifying pixel classes by taking into account both the pixel color distributions in several color spaces and their spatial arrangement in the image. In order to overcome the difficult problem of the color space choice, the algorithm selects the color space that is well suited to construct the class at each iteration step. The selection criterion is based on connectedness and color homogeneity measures of pixel subsets. In order to tune the sensitivity of segmentation, we introduce a hierarchical criterion that allows us to segment images with different numbers of regions as human observers do. Experiments carried out on the well-known Berkeley segmentation dataset show that this multicolor space approach succeeds in constructing classes that effectively correspond to regions in the image.

Paper Details

Date Published: 21 April 2015
PDF: 20 pages
J. Electron. Imag. 24(2) 023032 doi: 10.1117/1.JEI.24.2.023032
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Nicolas Vandenbroucke, Univ. du Littoral Côte d'Opale (France)
Laurent Busin, TIAMA (France)
Ludovic Macaire, Univ. des Sciences et Technologies de Lille (France)

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