A Graph-Based Image Segmentation Alorithm Using Heirarchical Social Metaheuristic
Abraham Duarte (Rey Juan Carlos University, Spain), Angel Sanchez (Rey Juan Carlos University, Spain), Felipe Fernandez (Polytechnique University of Madrid, Spain) and Antonio S. Montemayor (Rey Juan Carlos University, Spain)
Copyright: © 2006
This chapter proposes a new evolutionary graph-based image segmentation method to improve quality results. Our approach is quite general and can be considered as a pixel- or region-based segmentation technique. What is more important is that they (pixels or regions) are not necessarily adjacent. We start from an image described by a simplified undirected weighted graph where nodes represent either pixels or regions (obtained after an oversegmentation process) and weighted edges measure the dissimilarity between pairs of pixels or regions. As a second phase, the resulting graph is successively partitioned into two subgraphs in a hierarchical fashion, corresponding to the two most significant components of the actual image, until a termination condition is met. This graph-partitioning task is solved as a variant of the min-cut problem (normalized cut) using a hierarchical social (HS) metaheuristic. As a consequence of this iterative graph bipartition stage, pixels or regions are initially merged into the two most coherent components, which are successively bipartitioned according to this graph-splitting scheme. We applied the proposed approach to brightness segmentation on different standard test images, with good visual and objective segmentation quality results.