Region Merging for Severe Oversegmented Images Using a Hierarchical Social Metaheuristic

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.

• Type: Conference
• Year: 2005
• Where: EVOW
• Authors: Abraham Duarte, Ángel Sánchez, Felipe Fernández, Antonio Sanz
• Keywords: Artificial Intelligence, EVOW 2005, Initial Segmented Image, Region Adjacency Graph, Region Merging Method