2017/04/26 by Ke Wei, Wei Ke, Ke Yin +7 · 1 citation
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #Algorithm #Artificial intelligence #Augmented Lagrangian method #Benchmark (surveying) #Bernoulli distribution #Bernoulli's principle #Cluster analysis #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Image segmentation #Mathematics #Medical Image Segmentation Techniques #Physics #Potts model #Random variable #Segmentation #Statistical physics #Statistics #cs.CV
paper · pdf · doi:10.48550/arxiv.1704.08218
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/04/26 · openalex publication_date 2017/04/26 · arxiv updated 2017/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We propose an effective framework for multi-phase image segmentation and semi-supervised data clustering by introducing a novel region force term into the Potts model. Assume the probability that a pixel or a data point belongs to each class is known a priori. We show that the corresponding indicator function obeys the Bernoulli distribution and the new region force function can be computed as the negative log-likelihood function under the Bernoulli distribution. We solve the Potts model by the primal-dual hybrid gradient method and the augmented Lagrangian method, which are based on two different dual problems of the same primal problem. Empirical evaluations of the Potts model with the new region force function on benchmark problems show that it is competitive with existing variational methods in both image segmentation and semi-supervised data clustering.