Hybrid Color Segmentation Method Using a Customized Nonlinear Similarity Function

Abstract

Image segmentation is a fundamental step in several image processing tasks. It is a process where an image is divided into its constituent regions guided by a similarity criterion. One very interesting image segmentation method is the color structure code (CSC), which combines simultaneously split-and-merge and region-growing techniques. In this paper, a segmentation approach based on the CSC method, weighted color structure code (WCSC), is proposed. This method is guided by a nonlinear discrimination function, where the user-inference is captured by the Polynomial Mahalanobis distance, prioritizing, during the merging process, the regions with higher similarity to the user selected pattern. The WCSC has color distribution pattern-oriented characteristic, showing better coherence among the segments with higher similarity to the selected pattern. A qualitative evaluation and parametric paired analysis were performed to compare CSC, WCSC and other segmentation methods results, using images from Berkeley benchmark. The results from these comparison indicate an improvement on the segmentation result obtained by the WCSC.