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)

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 languageEnglish
Title of host publicationProceedings of the 2000 Congress on Evolutionary Computation, CEC 2000
PublisherIEEE Computer Society
Pages272-276
Number of pages5
DOIs
Publication statusPublished - 1 Jan 2000
Event2000 Congress on Evolutionary Computation, CEC 2000 - San Diego, CA, United States
Duration: 16 Jul 200019 Jul 2000

Conference

Conference2000 Congress on Evolutionary Computation, CEC 2000
Country/TerritoryUnited States
CitySan Diego, CA
Period16/07/0019/07/00

ASJC Scopus subject areas

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

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