Region Merging for Severe Oversegmented Images Using a Hierarchical Social Metaheuristic Export

@incollection{citeulike:1340540, 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.}, author = {Duarte, Abraham and S\'{s}nchez, \'{A}ngel and Fern\'{a}ndez, Felipe and Sanz, Antonio}, citeulike-article-id = {1340540}, citeulike-linkout-0 = {http://www.springerlink.com/content/remklp03t750nv4t}, journal = {Applications on Evolutionary Computing}, keywords = {cut, graph, watershed}, pages = {345--355}, posted-at = {2007-05-29 10:21:37}, priority = {2}, title = {Region Merging for Severe Oversegmented Images Using a Hierarchical Social Metaheuristic}, url = {http://www.springerlink.com/content/remklp03t750nv4t}, year = {2005} }

TY - CHAP ID - citeulike:1340540 L3 - citeulike-article-id:1340540 N2 - 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. JF - Applications on Evolutionary Computing EP - 355 TI - Region Merging for Severe Oversegmented Images Using a Hierarchical Social Metaheuristic SP - 345 KW - cut KW - graph KW - watershed AU - Duarte, Abraham AU - Sśnchez, Ángel AU - Fernández, Felipe AU - Sanz, Antonio PY - 2005/// UR - http://www.springerlink.com/content/remklp03t750nv4t ER -