Color-texture segmentation using JSEG based on Gaussian mixture modeling

1.INTRODUCTIONColor i mage segmentation is useful in many applica-tions.Fromthe segmentationresults,it is possible toidentify regions of interest and objectsinthe scene.Avariety of techniques have been proposed,for exam-ple:stochastic model based approaches1,4,9],mor-phological watershed based region growing11],energydiffusion10],and graph partitioning7].However,due tothe difficult nature of the problem,there are few auto-matic algorithms that can work well on alarge variety ofdata.Th…

Color-texture segmentation using JSEG based on Gaussian mixture modeling

An improved approach for J-value segmentation (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 (GMM) of image data 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 AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.