Band Ordering in Lossless Compression of Multispectral Images
IEEE Transactions on Computers
Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantization from Bayes factors with application to multilevel thresholding
Pattern Recognition Letters
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Multiband segmentation based on a hierarchical Markov model
Pattern Recognition
Visibility of wavelet quantization noise
IEEE Transactions on Image Processing
Limited color display for compressed image and video
IEEE Transactions on Circuits and Systems for Video Technology
A Multicomponent Image Segmentation Framework
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Bayesian mixture models of variable dimension for image segmentation
Computer Methods and Programs in Biomedicine
Model-based segmentation of multimodal images
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
Smooth image segmentation by nonparametric bayesian inference
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We consider the problem of multiband image clustering and segmentation. We propose a new methodology for doing this, called model-based cluster trees. This is grounded in model-based clustering, which bases inference on finite mixture models estimated by maximum likelihood using the EM algorithm, and automatically chooses the number of clusters by Bayesian model selection, approximated using BIC, the Bayesian Information Criterion. For segmentation, model-based clustering is based on a Markov spatial dependence model. In the Markov model case, the Bayesian model selection criterion takes account of spatial neighborhood information, and is termed PLIC, the Pseudolikelihood Information Criterion. We build a cluster tree by first segmenting an image band, then using the second band to cluster each of the level 1 clusters, and continuing if required for further bands. The tree is pruned automatically as a part of the algorithm by using Bayesian model selection to choose the number of clusters at each stage. An efficient algorithm for implementing the methodology is proposed. An example is used to evaluate this new approach, and the advantages and disadvantages of alternative approaches to multiband segmentation and clustering are discussed.