Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Image Segmentation Method Based on Fuzzy Entropy and Grey Relational Analysis
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Approximation Degrees in Decision Reduct-Based MRI Segmentation
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
International Journal of Remote Sensing
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Engineering Applications of Artificial Intelligence
Adaptive Rough Entropy Clustering Algorithms in Image Segmentation
Fundamenta Informaticae
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Fuzzy clustering method for image segmentation usually needs the determination of the cluster number in advance. Therefore, an adaptive fuzzy clustering image segmentation algorithm based on jumping gene genetic algorithm (JGGA) is investigated in this paper. A new weighted multi-objective evaluation function considering the cluster number, the inner-class distance and the inter-class distance is proposed. Because the cluster number is uncertain during the optimization process, a variable-length JGGA (VJGGA) is introduced. The cluster number and the cluster centers of the image gray values are determined by the minimization of evaluation function. Simulation results of the segmentation for a real image indicate that VJGGA algorithm is characterized by strong global capability of searching the optimal segmentation number and cluster centers, compared with variable-length GA (VGA).