Abstract
AbstractIn 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
Highlights
Image segmentation consists of partitioning an entire image into different regions, which are similar in some predefined manner.1,2 Segmentation is an important feature of human visual perception, which manifests itself spontaneously and naturally
(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
By the use and analysis of receiver operating characteristic (ROC)13 curves and graphs, we obtained some proper characteristics of the segmentation method under study such as its stability related to the threshold selection and to the selection of an appropriate number of pixels required by the color samples
Summary
Image segmentation consists of partitioning an entire image into different regions, which are similar in some predefined manner. Segmentation is an important feature of human visual perception, which manifests itself spontaneously and naturally. Bai and Sapiro present a semi-automatic algorithm for natural images and video segmentation.27 Their technique is based on calculating a weighted geodesic distance from every pixel to user-generated scribbles. The result of a particular similarity function calculation for every pixel and the color centroid (meaning the similarity measure between the pixel and the representative color value) generates the CSI The generation of this image is the basis of our method and preserves the information of the color selected from the original color image. From this sample of pixels, we calculate the statistical indicators according to our modified HSI color model, which can be consulted in Alvarado (2006) This information is necessary to adapt the color similarity function in order to obtain good results. The statistical values needed in Eq [1] are calculated as follows:
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