Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bootstrap technique in cluster analysis
Pattern Recognition
How many clusters are best?—an experiment
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Cluster Analysis
A Bayesian Segmentation Methodology for Parametric Image Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting Images Corrupted by Correlated Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative Analysis of MR Brain Image Sequences by Adaptive Self-Organizing Finite Mixtures
Journal of VLSI Signal Processing Systems - special issue on applications of neural networks in biomedical image processing
Statistical Modeling of Colour Data
International Journal of Computer Vision
Unsupervised Color Image Segmentation Using Compound Markov Random Field Model
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Computers in Biology and Medicine
On considering uncertainty and alternatives in low-level vision
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Estimation of the number of clusters using multiple clustering validity indices
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Journal of Visual Communication and Image Representation
| Hi-index | 0.14 |
A clustering scheme is used for model parameter estimation. Most of the existing clustering procedures require prior knowledge of the number of classes, which is often, as in unsupervised image segmentation, unavailable and must be estimated. This problem is known as the cluster validation problem. For unsupervised image segmentation the solution of this problem directly affects the quality of the segmentation. A model-fitting approach to the cluster validation problem based on Akaike's information criterion is proposed, and its efficacy and robustness are demonstrated through experimental results for synthetic mixture data and image data.