Abstract
We propose a color image segmentation approach based on rough set theory elements. Main con- tributions of the proposed approach are twofold. First, by using an adaptive threshold selection, the approach is automatically adjustable according to the image content. Second, a region-merging process, which takes into account both features and spatial relations of the resulting segments, lets us minimize over-segmentation issues. These two proposals allow our method to overcome some performance issues shown by previous rough set theory-based approaches. In addition, a study to determine the best suited color representation for our segmentation approach is carried out, determining that the best results are obtained using a perceptually uniform color space. A set of qualitative and quantitative tests over a comprehensive image database shows that the proposed method produces high-quality segmentation outcomes, better than those obtained using the pre- vious rough set theory-based and standard segmentation approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JEI.23.1.013024)
Highlights
Color image segmentation has been a central problem in computer vision and pattern recognition systems for many years
We have mainly explored the use of perceptual color spaces CIELab and CIELuv, because it has been found that they are the most appropriate when the resemblance to the human visual system is desirable
The BDSDS500 is an extension of the Berkeley Segmentation Data Set 300 (BSDS300)
Summary
Color image segmentation has been a central problem in computer vision and pattern recognition systems for many years. Some problems related with the histogrambased methods have been addressed by the RBM, a number of issues are still observed Considering those limitations, in this article, we put forward a set of modifications in order to improve the segmentation performance: [1] a different threshold selection method, allowing an automatic adaptation of the peak selection criteria for a given image, [2] an adaptation of a region-merging process, which considers both the features and the spatial relationships among the resulting segments in the image. The lower approximation is a description of the universe of objects that are known with certainty, whereas the upper approximation is the description of the objects that possibly belong to the set From this concept of a rough set and in the context of image segmentation with histogram-based methods, Mohabey and Ray have developed the idea of the histon which can be considered as the upper approximation of a rough set. We use three color channels; it is worth to remark that it is possible to expand this approach for images that have more than three information channels
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