Improving Image Segmentation Quality through Effective Region Merging using a Hierarchical Social Metaheuristic Abraham Duarte 1 , Ángel Sánchez 1 , Felipe Fernández 2 and Antonio S. Montemayor 1 1 ESCET-URJC, Campus de Móstoles, 28933, Madrid, Spain {abraham.duarte, angel.sanchez, antonio.sanz}@urjc.es 2 Dept. Tecnología Fotónica, FI-UPM, Campus de Montegancedo, 28660, Madrid, Spain Felipe.Fernandez@es.bosch.com Abstract. This paper proposes a new evolutionary region merging method in order to efficiently improve segmentation quality results. Our approach starts from an oversegmented image, which is obtained by applying a standard morphological watershed transformation on the original image. Next, each resulting region is represented by its centroid. The oversegmented image is described by a simplified undirected weighted graph, where each node represents one region and weighted edges measure the dissimilarity between pairs of regions (adjacent and non-adjacent) according to their intensities, spatial locations and original sizes. Finally, the resulting graph is iteratively partitioned in a hierarchical fashion into two subgraphs, corresponding to the two most significant components of the actual image, until a termination condition is met. This graph-partitioning task is solved by a variant of the min-cut problem (normalized cut) using a Hierarchical Social (HS) metaheuristic. We have efficiently applied the proposed approach to brightness segmentation on different standard test images, with good visual and objective segmentation quality results. Keywords: Evolutionary metaheuristics, image segmentation, Watershed, region merging, graph-based segmentation, Hierarchical Social Algorithm. 1 INTRODUCTION Image segmentation is one of the most complex stages in image analysis. It becomes essential for subsequent image description and recognition tasks. The problem consists of partitioning an image into its constituent regions, objects or labels [12]. The level of division depends on the specific problem being solved. This partition is accomplished in such a way that the pixels belonging to homogeneous regions, regarding to one or more features (i.e. brightness, texture or colour), share the same label, and regions of pixels with significantly different features have different labels. There must be considered four objectives for developing an efficient generalized segmentation algorithm [15]: continuous closed contours, non-oversegmentation, independence of threshold setting and short computation time. In particular, the oversegmentation error, which occurs when a single semantic object is divided into several regions, is a tendency of some segmentation methods