General scheme of region competition based on scale space
In this paper, we propose a general scheme of region competition (GSRC) for image segmentation based on scale space. First, we present a novel classification algorithm to cluster the image feature data according to the generally defined peaks under a certain scale and a scale space-based classification scheme to classify the pixels by grouping the resultant feature data clusters into several classes with a standard classification algorithm. Second, to reduce the resultant segmentation error, we develop a nonparametric probability model from which the functional for GSRC is derived. Third, we design a general and formal approach to automatically determine the initial regions. Finally, we propose the kernel procedure of GSRC which segments an image by minimizing the functional. The strategy adopted by GSRC is first to label pixels whose corresponding regions can be determined in large likelihood, and then to fine-tune the final regions with the help of the nonparametric probability model, boundary smoothing, and region competition. GSRC quantitatively controls the segmentation extent with the scale space-based classification scheme. Although the description of the scheme is nonparametric in this paper, GSRC can also work parametrically if all nonparametric procedures in this paper are substituted with the parametric counterparts.
region competition segmentation scale space-based classification nonparametric probability model region growing