A hierarchical distributed genetic algorithm for image segmentation

Hanchuan Peng, Fuhui Long, Zheru Chi, Wanchi Su

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 2000 Congress on Evolutionary Computation, CEC 2000
PublisherIEEE Computer Society
Number of pages5
Publication statusPublished - 1 Jan 2000
Event2000 Congress on Evolutionary Computation, CEC 2000 - San Diego, CA, United States
Duration: 16 Jul 200019 Jul 2000


Conference2000 Congress on Evolutionary Computation, CEC 2000
Country/TerritoryUnited States
CitySan Diego, CA

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science(all)
  • Engineering(all)

Cite this