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

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 appli...

• Type: Journal
• Year: 2006
• Where: PRL
• Authors: Abraham Duarte, Miguel Ángel Sánchez Vidales, Felipe Fernández, Antonio S. Montemayor
• Keywords: Images, Morphological Watershed Transformation, Oversegmented Image, PRL 2006