The cardinal motivation for image segmentation is twofold. It not only provides an end user with the flexibility to efficiently access and manipulate individual content, but also furnishes a compact representation of the data wherein all subsequent processing can be done at a region/segment level as opposed to the pixel level, resulting in potentially significant computational gains. To this effect, segmentation is predominantly employed as a preprocessing step to annotate, enhance, analyze, classify, categorize, and/or abstract information from images. In general, there are many applications for color image segmentation in the image processing, computer vision, and pattern recognition fields, including content-based image retrieval (CBIR), image rendering, region classification, segment-based compression, surveillance, perceptual ranking of regions, graphics, and multimedia to name a few. Furthermore, many approaches have been developed in other modalities of imaging such as remote sensing (multi/hyperspectral data) and biomedical imaging [computed tomography (CT)], positron emission tomography (PET), and magnetic resonance imaging (MRI) data for sophisticated applications such as large area search, three-dimensional (3-D) modeling, visualization, and navigation. The exponential growth of the number of applications that employ segmentation in itself provides a strong motivation for continued research and development.