Color image segmentation using competitive learning

Author

Uchiyama, Toshio ; Arbib, Michael A.

Author_Institution

NTT DATA Commun. Syst. Corp., Kawasaki, Japan

Volume

16

Issue

12

fYear

1994

fDate

12/1/1994 12:00:00 AM

Firstpage

1197

Lastpage

1206

Abstract

Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results

Keywords

convergence of numerical methods; image colour analysis; image segmentation; least squares approximations; unsupervised learning; vector quantisation; color coordinates; color image segmentation; color scenes; color space clustering; competitive learning; convergence; efficiency; least sum-of-squares criterion; optimum solution approximation; Clustering algorithms; Computer performance; Computer vision; Image color analysis; Image converters; Image segmentation; Layout; Multidimensional systems; Shape; Vector quantization;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.387488

Filename

387488