Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Objective and quantitative segmentation evaluation and comparison
Signal Processing
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Computer Vision and Image Understanding
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
Genetic Learning for Adaptive Image Segmentation
Genetic Learning for Adaptive Image Segmentation
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Measuring the component overlapping in the Gaussian mixture model
Data Mining and Knowledge Discovery
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In this work, a method is described for evolving adaptive procedures for colour image segmentation. We formulate the segmentation problem as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms (GA) for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into GA, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of GA to automatic and unsupervised texture segmentation. Some examples in Colour Maps are presented and overall results discussed.