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.

• Type: Conference
• Year: 2004
• Where: EVOW
• Authors: Abraham Duarte, Ángel Sánchez, Felipe Fernández, Antonio S. Montemayor, Juan José Pantrigo
• Keywords: Artificial Intelligence, EVOW 2004, Normalized Cut, Normalized Cut Computation, Simplified Image Graph