title: An evolutionary approach for image segmentation.
abstract: The paper explores the use of evolutionary techniques in dealing with the image segmentation problem. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A genetic algorithm that uses a fitness function based on an extension of the normalized cut criterion is proposed. The algorithm employs the locus-based representation of individuals, which allows for the partitioning of images without setting the number of segments beforehand. A new concept of nearest neighbor that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is also defined. Experimental results show that our approach is able to segment images in a number of regions that conform well to human visual perception. The visual perceptiveness is substantiated by objective evaluation methods based on uniformity of pixels inside a region, and comparison with ground-truth segmentations available for part of the used test images.
authors.affiliation: Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), Via P. Bucci 41C, 87036 Rende, Italy amelio@icar.cnr.it.
authors.forename: Alessia
authors.initials: A
authors.lastname: Amelio
authors.forename: Clara
authors.initials: C
authors.lastname: Pizzuti
mesh_terms: D000465:Algorithms; D003205:Computing Methodologies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D014796:Visual Perception / Q000502:physiology*
keywords: Image segmentation; evolutionary computation; genetic algorithms; normalized cut