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

Authors
Journal
Pattern Recognition Letters
0167-8655
Publisher
Elsevier
Publication Date
Volume
27
Issue
11
Identifiers
DOI: 10.1016/j.patrec.2005.07.022
Keywords
  • Evolutionary Metaheuristics
  • Image Segmentation
  • Watershed
  • Region Merging
  • Graph-Based Segmentation
  • Hierarchical Social Algorithm

Abstract

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.

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