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EVOW
2004
Springer

Top-Down Evolutionary Image Segmentation Using a Hierarchical Social Metaheuristic

14 years 28 days ago
Top-Down Evolutionary Image Segmentation Using a Hierarchical Social Metaheuristic
Abstract. This paper presents an application of a hierarchical social (HS) metaheuristic to region-based segmentation. The original image is modelled as a simplified image graph, which is successively partitioned into two regions, corresponding to the most significant components of the actual image, until a termination condition is met. The graph-partitioning task is solved as a variant of the min-cut problem (normalized cut) using an HS metaheuristic. The computational efficiency of the proposed algorithm for the normalized cut computation improves the performance of a standard genetic algorithm. We applied the HS approach to brightness segmentation on various synthetic and real images, with stimulating trade-off results between execution time and segmentation quality.
Abraham Duarte, Ángel Sánchez, Felip
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where EVOW
Authors Abraham Duarte, Ángel Sánchez, Felipe Fernández, Antonio S. Montemayor, Juan José Pantrigo
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