Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
ACM Computing Surveys (CSUR)
Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters
Information Sciences: an International Journal
Region merging techniques using information theory statistical measures
IEEE Transactions on Image Processing
Where Are the Niches? Dynamic Fitness Sharing
IEEE Transactions on Evolutionary Computation
One-pixel-wide closed boundary identification
IEEE Transactions on Image Processing
Hi-index | 0.00 |
In this paper, a novel genetic clustering algorithm based on dynamic niching (DNGA) for image segmentation is proposed. It is an effective and robust approach to image segmentation on the basis of a total similarity function relating to the approximate density shape estimation. In the new algorithm, a dynamic identification of the niches is performed at each generation to automatically evolve the proper number of clusters and appropriate cluster centers of the data set. Moreover, a local search method is embeded in the evolutionary process which makes the dynamic niching method insensitive to the radius of the niche. Compared to existing methods, DNGA algorithm does not need to pre-specify the number of segmentation. Several images are used to demonstrate its superiority. The experimental results show that DNGA algorithm has high performance, effectiveness and flexibility.