An Adaptive Color Similarity Function for Color Image Segmentation

Abstract

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.

Improved HSI Color Space for Color Image Segmentation

“…where Slope is a parameter representing the degree of affectation of the exponential factor to the saturation attribute for every pixel; abs( ) represents the absolute value function and intensity( ) is defined as an average of the RGB channels in Eq. (6) in [5]. After calculating the New_Saturation values for the source image, it is thresholded with the saturation threshold (st) to obtain the effects of the improved achromatic zone G. We found good performance with the parameters empirically determined in the following ranges: st ϵ [0.07, 0.1], ip ϵ [0.5, 0.6], and Slope ϵ [1, 1.5].…”

“…The pixel sample is a representation of the desired color(s) to be segmented from a color image. From this pixel sample we calculate two values to feed our segmentation algorithm: the color centroid and a measure of the dispersion from this centroid, in our case the standard deviation, they can be consulted for reference in [5] and [10].…”

“…In order to model better the human visual response in the abrupt corners regions near the union zones of the two cones with the cylinder of the singularity zone G, as presented in [5], [7] and [10], we found convenient to modify the characterization of the HSI color model for colors belonging to regions with very low or very high brightness. This step will permit us to differentiate the colors in these extreme regions, because the human response to brightness does not have this possibility.…”

“…The results tabulated in Section 3 show that the segmentation method presented in this paper offers a useful and efficient alternative for the segmentation of objects with different colors in relatively complex color images with good performance in the presence of the unavoidable additive noise. It has also good performance in gray level and low contrast images [5].…”

“…One weakness in the characterization of the achromatic region as presented in [5], [7] and [10] is its poor performance in the border regions of achromatic and chromatic zones, both in low and high brightness, due to the fact that the commonly used HSI color model does not take into account the human visual response at low and high brightness. Human vision has a nonlinear perceptual response to luminance [11].…”