Unsupervised color–texture segmentation based on soft criterion with adaptive mean-shift clustering

An improved approach for J value segmentation (JSEG) is presented for unsupervised color–texture segmentation. Instead of the color quantization algorithm used in JSEG, an automatic classification method using adaptive mean-shift (AMS) clustering is applied for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling 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 improved method overcomes the limitations of JSEG successfully and is more robust.