Unsupervised image segmentation based on contourlet texture features and byy harmony learning of t-mixtures

Abstract

This paper proposes an unsupervised color image segmentation approach excellent in multi-texture image segmentation. Actually, it employs a novel texture feature extraction mechanism through the contourlet subband coefficient clustering, which is more effective in image segmentation than the discrete cosine transform based normalization technique (DCT). In addition, it adopts the gradient Bayesian Ying-Yang harmony learning of t-mixtures (BYY-t) for automatic image objects detection so that the image segmentation is in an unsupervised mode. The experiments on the images in Berkeley Segmentation Database and Benchmark (BSDB) database demonstrate the improved performances of this approach in varied and complex color image segmentation. Additional experiments on multi-texture color images further demonstrate its better performances in comparison with those of the state-of-art algorithms. © 2014 Springer International Publishing Switzerland.