New region force for variational models in image segmentation and high dimensional data clustering
| Autor: | Ke Yin, Xue-Cheng Tai, Ke Wei, Tony F. Chan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Clustering high-dimensional data Augmented Lagrangian method Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology General Medicine Image segmentation 01 natural sciences 010101 applied mathematics Indicator function Bernoulli distribution 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0101 mathematics Cluster analysis Gradient method Algorithm Potts model |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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