An interactive, semiautomatic image segmentation method is presented which, unlike most of the existing methods in the published literature, processes the color information of each pixel as a unit, thus avoiding color information scattering. The process has two steps: 1) The manual selection of few sample pixels of the color to be segmented, 2) The automatic generation of the so called Color Similarity Image (CSI), which is a gray level image with all the tonalities of the selected color. The color information of every pixel is integrated by a similarity function for direct color comparisons. The color integrating technique is direct, simple, and computationally inexpensive. It is shown that the improvement in quality of our proposed segmentation technique and its quick result is significant with respect to other solutions found in the literature.

In this paper an interactive, semiautomatic image segmentation method is presented which, processes the color information of each pixel as a unit, thus avoiding color information scattering. The process has only two steps: 1) The manual selection of few sample pixels of the color to be segmented in the image; and 2) The automatic generation of the so called Color Similarity Image (CSI), which is just a gray level image with all the tonalities of the selected colors. The color information of every pixel is integrated in the segmented image by an adaptive color similarity function designed for direct color comparisons. The color integrating technique is direct, simple, and computationally inexpensive and it has also good performance in gray level and low contrast images.

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

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