Title :
Color image segmentation using competitive learning
Author :
Uchiyama, Toshio ; Arbib, Michael A.
Author_Institution :
NTT DATA Commun. Syst. Corp., Kawasaki, Japan
fDate :
12/1/1994 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
