An adaptive bayesian clustering and multivariate region merging based technique for efficient segmentation of color images

We propose a methodology for improved segmentation of images in a Bayesian framework by fusion of color, texture and gradient information. The proposed algorithm is initialized by subjecting the input image to an adaptive clustering scheme for initial region formation. Following this, a vector field approach is employed to split regions comprising of strong edges. Subsequently, all spatially independent regions are provided separate region labels, given that most clustering approaches neglect the spatial association among them. The resultant region map is integrated with textural features based on co-occurrence matrices, in a unique multivariate merging procedure hierarchically fusing regions with strong similarities. The merging process is eventually terminated using a Receiver Operating Characteristic analysis to determine the optimal number of segments in the final segmentation. Performance evaluation on the Berkeley segmentation dataset demonstrates that our approach outperforms a family of published techniques, with superior segmentation quality.