Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified approach for image segmentation using exact statistics
Computer Vision, Graphics, and Image Processing
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A systematic way for region-based image segmentation based on Markov Random Field model
Pattern Recognition Letters
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image processing and applications
Color image processing and applications
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
IRGS: Image Segmentation Using Edge Penalties and Region Growing
IEEE Transactions on Pattern Analysis and Machine Intelligence
EM algorithm for image segmentation initialized by a tree structure scheme
IEEE Transactions on Image Processing
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
IEEE Transactions on Image Processing
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Pixon-based image segmentation with Markov random fields
IEEE Transactions on Image Processing
Spatiotemporal video segmentation based on graphical models
IEEE Transactions on Image Processing
Unsupervised multiscale color image segmentation based on MDL principle
IEEE Transactions on Image Processing
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs
IEEE Transactions on Image Processing
A robust patch-statistical active contour model for image segmentation
Pattern Recognition Letters
A multi-object segmentation algorithm based on background modeling and region growing
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
Hi-index | 0.01 |
Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness and solution space complexity that impose computational cost and degrade algorithmic robustness. To deal with these problems, a Markov random field (MRF) based multivariate segmentation algorithm called "multivariate iterative region growing using semantics" (MIRGS) is presented. In MIRGS, the impact of intraclass variation and computational cost are reduced using the MRF spatial context model incorporated with adaptive edge penalty and applied to regions. Semantic region growing starting from watershed over-segmentation and performed alternatively with segmentation gradually reduces the solution space size, which improves segmentation effectiveness. As a multivariate iterative algorithm, MIRGS is highly sensitive to initial conditions. To suppress initialization sensitivity, it employs a region-level k-means (RKM) based initialization method, which consistently provides accurate initial conditions at low computational cost. Experiments show the superiority of RKM relative to two commonly used initialization methods. Segmentation tests on a variety of synthetic and natural multivariate images demonstrate that MIRGS consistently outperforms three other published algorithms.