Abstract
A novel hierarchical distributed genetic algorithm is proposed for image segmentation. Firstly, a technique of histogram dichotomy is proposed to explore the statistical property of input image and produce a hierarchical quantization image. Then a hierarchical distributed genetic algorithm (HDGA) is imposed on the quantized image to explore the spatial connectivity and produce final segmentation result. HDGA is a major improvement of the original distributed genetic algorithm (DGA) and multiscale distributed genetic algorithm (MDGA) in four aspects: (1) HDGA does not require the a priori number of image regions, however it can effectively and adaptively control the segmentation quality; (2) the chromosome structure is revised from the original label (multilabel)- condition-fitness format to a more compact (storage-efficient) label-fitness format; (3) the fitness function is revised to utilize the spatial connectivity, but not the original "reconstruction" error; (4) three revised genetic operations are presented to make the algorithm computation-efficient. Our experiments give proofs for the advantages of HDGA.
Original language | English |
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Title of host publication | Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000 |
Publisher | IEEE Computer Society |
Pages | 272-276 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 1 Jan 2000 |
Event | 2000 Congress on Evolutionary Computation, CEC 2000 - San Diego, CA, United States Duration: 16 Jul 2000 → 19 Jul 2000 |
Conference
Conference | 2000 Congress on Evolutionary Computation, CEC 2000 |
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Country/Territory | United States |
City | San Diego, CA |
Period | 16/07/00 → 19/07/00 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- General Computer Science
- General Engineering