Algorithms for clustering data
Algorithms for clustering data
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
Robust mixture modelling using the t distribution
Statistics and Computing
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
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
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
Majorization-minimization mixture model determination in image segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
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We present a new finite mixture model for image segmentation. Firstly, in order to take into account the spatial dependencies in an image, existing mixture models use a constant temperature parameter (@b) throughout the image for every label. The constant value of @b reduces the impact of noise in homogeneous regions but negatively affects segmentation along the border of two regions. We propose a new way to use a different value of @b throughout the image. Secondly, in order to incorporate the correlation between each center pixel and its neighboring pixels, the existing mixture model gives the same importance to all pixels in a neighborhood window. We assign different weights to different pixels appearing in the window, which is based on the fact that the clique strength should be reduced with distance. Thirdly, our model is based on the Student's-t distribution, which is heavily tailed and more robust than the Gaussian. We exploit the Dirichlet distribution and Dirichlet law to incorporate the spatial relationships between pixels in an image. Finally, the expectation maximization (EM) algorithm is adopted to maximize the data log-likelihood and to optimize the parameters. The performance is compared to other existing models based on the model-based techniques, demonstrating the superiority of the proposed model for image segmentation.