Color Image Segmentation Using Rival Penalized Controlled Competitive Learning

Lap Tak Law*, Yiu Ming Cheung

*Corresponding author for this work

Research output: Contribution to conferenceConference paperpeer-review

37 Citations (Scopus)


Color image segmentation has been extensively applied to a lot of applications such as pattern recognition, image compression and matching. In the literature, conventional k-means (MacQueen 1967) is one common algorithm used in pixel-based image segmentation. However, it needs to pre-assign an appropriate cluster number before performing clustering, which is an intractable problem from a practical viewpoint. In contrast, the recently proposed Rival Penalization Controlled Competitive Learning (RPCCL) approach (Cheung 2002) can perform correct clustering without knowing the exact cluster number in analog with the RPCL (Xu et al. 1993). The RPCCL penalizes the rivals with a strength control such that extra seed points are automatically driven far away from the input data set, but without the de-learning rate selecting problem as the RPCL. In this paper, we further investigate the RPCCL on color image segmentation in comparison with the k-means and RPCL algorithms.

Original languageEnglish
Number of pages5
Publication statusPublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 20 Jul 200324 Jul 2003


ConferenceInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR

Scopus Subject Areas

  • Software
  • Artificial Intelligence


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