Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
Algorithms for clustering data
Algorithms for clustering data
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Hidden Markov Measure Field Models for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
Flexible background mixture models for foreground segmentation
Image and Vision Computing
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The mean field theory in EM procedures for Markov random fields
IEEE Transactions on Signal Processing
IEEE Transactions on Fuzzy Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
A temporally adaptive classifier for multispectral imagery
IEEE Transactions on Neural Networks
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation
IEEE Transactions on Neural Networks
Variational and PCA based natural image segmentation
Pattern Recognition
A finite mixture model for detail-preserving image segmentation
Signal Processing
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Gaussian mixture model based on the Dirichlet distribution (Dirichlet Gaussian mixture model) has recently received great attention for modeling and processing data. This paper studies the new Dirichlet Gaussian mixture model for image segmentation. First, we propose a new way to incorporate the local spatial information between neighboring pixels based on the Dirichlet distribution. The main advantage is its simplicity, ease of implementation and fast computational speed. Secondly, existing Dirichlet Gaussian model uses complex log-likelihood function and require many parameters that are difficult to estimate. The total parameters in the proposed model lesser and the log-likelihood function have a simpler form. Finally, to estimate the parameters of the proposed Dirichlet Gaussian mixture model, a gradient method is adopted to minimize the negative log-likelihood function. Numerical experiments are conducted using the proposed model on various synthetic, natural and color images. We demonstrate through extensive simulations that the proposed model is superior to other algorithms based on the model-based techniques for image segmentation.