New region force for variational models in image segmentation and high dimensional data clustering
This work addresses image segmentation and data clustering problems for computer vision and data analysis, but it is incremental as it builds on existing variational methods.
The paper tackled multi-phase image segmentation and semi-supervised data clustering by introducing a novel region force term into the Potts model, showing competitive performance with existing variational methods on benchmark problems.
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.