Improving image segmentation quality through effective region merging using a hierarchical social metaheuristic

  • Authors:

  • Abraham Duarte;Ángel Sánchez;Felipe Fernández;Antonio S. Montemayor

  • Affiliations:

  • ESCET-URJC, Campus de Móstoles, 28933 Madrid, Spain;ESCET-URJC, Campus de Móstoles, 28933 Madrid, Spain;Dept. Tecnología Fotónica, FI-UPM, Campus de Montegancedo, 28660 Madrid, Spain;ESCET-URJC, Campus de Móstoles, 28933 Madrid, Spain

  • Venue:
  • Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
  • Year:
  • 2006

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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.