Finding Curvilinear Features in Spatial Point Patterns: Principal Curve Clustering with Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Variational Bayesian image modelling
ICML '05 Proceedings of the 22nd international conference on Machine learning
A finite mixture model for image segmentation
Statistics and Computing
Field Sampling from a Segmented Image
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
The infinite Student's t-mixture for robust modeling
Signal Processing
| Hi-index | 0.14 |
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hidden Markov random field. Each number of colors considered is viewed as corresponding to a statistical model for the image, and the resulting models are compared via approximate Bayes factors. The Bayes factors are approximated using BIC (Bayesian Information Criterion), where the required maximized likelihood is approximated by the Qian-Titterington pseudolikelihood. We call the resulting criterion PLIC (Pseudolikelihood Information Criterion). We also discuss a simpler approximation, MMIC (Marginal Mixture Information Criterion), which is based only on the marginal distribution of pixel values. This turns out to be useful for initialization and it also has moderately good performance by itself when the amount of spatial dependence in an image is low. We apply PLIC and MMIC to a medical image segmentation problem.