Region merging for severe oversegmented images using a hierarchical social metaheuristic

Authors:
Abraham Duarte;Ángel Sśnchez;Felipe Fernández;Antonio Sanz
Affiliations:
Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Dept. Tecnología Fotónica, FI-UPM, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

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Abstract

This paper proposes a new evolutionary region merging method to improve segmentation quality result on oversegmented images. The initial segmented image is described by a modified Region Adjacency Graph model. In a second phase, this graph is successively 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 as a variant of the min-cut problem (normalized cut) using a Hierarchical Social (HS) metaheuristic. We applied the proposed approach on different standard test images, with high-quality visual and objective segmentation results.