Algorithms for clustering data
Algorithms for clustering data
A deterministic annealing approach to clustering
Pattern Recognition Letters
Elements of information theory
Elements of information theory
Multiscale minimization of global energy functions in some visual recovery problems
CVGIP: Image Understanding
A Framework for Performance Characterization of Intermediate-Level Grouping Modules
IEEE Transactions on Pattern Analysis and Machine Intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Multiscale annealing for grouping and unsupervised texture segmentation
Computer Vision and Image Understanding
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Unsupervised Image Clustering Using the Information Bottleneck Method
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Efficient region-based image retrieval
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Robust Image Segmentation Using Resampling and Shape Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Bayesian Image Segmentation
International Journal of Computer Vision
Image segmentation using histogram fitting and spatial information
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
An agglomerative segmentation framework for non-convex regions within uterine cervix images
Image and Vision Computing
Stable bounded canonical sets and image matching
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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Unsupervised Image Segmentation is one of the central issues in Computer Vision. From the viewpoint of exploratory data analysis, segmentation can be formulated as a clustering problem in which pixels or small image patches are grouped together based on local feature information. In this contribution, parametrical distributional clustering (PDC) is presented as a novel approach to image segmentation. In contrast to noise sensitive point measurements, local distributions of image features provide a statistically robust description of the local image properties. The segmentation technique is formulated as a generative model in the maximum likelihood framework. Moreover, there exists an insightful connection to the novel information theoretic concept of the Information Bottleneck (Tishby et al. [17]), which emphasizes the compromise between efficient coding of an image and preservation of characteristic information in the measured feature distributions.The search for good grouping solutions is posed as an optimization problem, which is solved by deterministic annealing techniques. In order to further increase the computational efficiency of the resulting segmentation algorithm, a multi-scale optimization scheme is developed. Finally, the performance of the novel model is demonstrated by segmentation of color images from the Corel data base.