One critical problem in image segmentation is how to explore the information in both feature and image space and incorporate them together. One approach in this direction is reported in this chapter. Watershed algorithm is traditionally applied on image domain but it fails to capture the global color distribution information. A new technique is to apply first the watershed algorithm in feature space to extract clusters with irregular shapes, and then to use feature space analysis in image space to get the final result by minimizing a global energy function based on Markov random field theory. Two efficient energy minimization algorithms: Graph cuts and highest confidence first (HCF) are explored under this framework. Experiments with real color images show that the proposed two-step segmentation framework is efficient and has been successful in various applications.
