Color Image Segmentation by Means of a Similarity Function

Abstract

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.

Influence of the Luminance L* during Color Segmentation in the L*a*b* Color Space

“…All the images had the same post-processing: elimination of areas smaller than 30 pixels and a morphological closing with a circular structuring element of radius equal to two pixels. The results of the application with the solution given by [6] of the color image segmentation with a different level of shadow fading (shown in every even row) compared with those obtained with the Euclidean metric in the L*a*b* rejecting L* (shown in every odd row) are included in Figure 4 for each color quadrant (0°, 60°, 120°, 180°, 240° and 300°) and at 10% increments of the shadow fading.…”

“…In this way the study of the influence of each individual parameter is possible. Comparative tests between an adaptive color similarity function [6] and the Euclidean metric in the L*a*b* color space [8] were performed. The manner in which the tests were implemented is as follows:…”

“…Samples of both background and figure were taken, from which centroid and standard color dispersion were calculated. Details can be consulted in [6]. 2.…”

“…3. For each pixel, the similarity function for the centroids of object and background was calculated creating two color similarity images (CSI) [6]. 4.…”

“…The synthetic images where designed to evaluate the efficiency of achieved color information from given segmentation algorithms and are explained in detail in Section 3. A comparative study with an adaptive color similarity function defined as a product of Gaussian functions in a modified HSI color space [6] is presented in Section 4. Finally, conclusions are given in Section 5.…”