Synergism in color image segmentation

Abstract

An improved approach for JSEG is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift (AMS) based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling of image data set for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on GMM overcomes the limitations of JSEG successfully and is more robust. © Springer-Verlag Berlin Heidelberg 2004.