Stick-Breaking Variational Bayesian Based Image Segmentation Algorithm

In order to improve the anti-noise robustness of image segmentation and adaptively determine the number of segmentations,Markov random field is established in the process of constructing the prior probability of clustering label,and the spatial correlation constraint is introduced into the probability modeling of the mixture model of the Dirichlet process,so that the spatial smoothness of clustering can be enhanced,and the convergent analytical solution of clustering label is obtained by the variational inference method.An image segmentation algorithm based on stick-breaking variational Bayesian is proposed,which realizes synchronous and adaptive learning of pixel clustering labels and segmentation numbers,and avoids the computational complexity caused by spatial correlation constraints in traditional methods.The numerical experiment results based on Berkeley BSD500 image test data set showed that the algorithm has better performance than the existing mixture model based image segmentation algorithms.The proposed algorithm has a higher PRI value and a better anti-noise robustness when the noise variance is lower than 0.1.