Variable structure neural network for adaptive color clustering

Jun Zhang*, Jinglu Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Color clustering is widely used for color image segmentation, transmission and visualization. However, traditional clustering algorithms have difficulty in adaptive determination of the proper cluster number. In this paper, a variable structure self-organizing map neural network (VSSOM) is proposed to achieve adaptive color clustering. It can provide the suitable cluster number and the palette for generating the clustered color image. The characteristic of the algorithm is that the output neuron of VSSOM is changed from 2 to N based on the defined learning rules during training. When the network converges, its N is the number of color clusters and the palette of clustered image is corresponding to the weights of the network. Experimental results show that the proposed algorithm has the desired ability for color clustering.

Original languageEnglish
Title of host publicationProceedings of the 7th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2010
PublisherActa Press
Pages248-252
Number of pages5
ISBN (Print)9780889868236
DOIs
Publication statusPublished - 2010
Event7th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2010 - Innsbruck, Austria
Duration: 2010 Feb 172010 Feb 19

Publication series

NameProceedings of the 7th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2010

Conference

Conference7th IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2010
Country/TerritoryAustria
CityInnsbruck
Period10/2/1710/2/19

Keywords

  • Color clustering
  • Neural network
  • Self-organizing map
  • Variable structure

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Communication

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