Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video sequence segmentation using genetic algorithms
Pattern Recognition Letters
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Genetic Algorithm-Based Segmentation for Automatic VOP Generation
IDMS/PROMS 2002 Proceedings of the Joint International Workshops on Interactive Distributed Multimedia Systems and Protocols for Multimedia Systems: Protocols and Systems for Interactive Distributed Multimedia
Multiscale Feature Extraction from the Visual Environment in an Active Vision System
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
A Real-Time Region-Based Motion Segmentation Using Adaptive Thresholding and K-Means Clustering
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Automatic video segmentation using genetic algorithms
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
International Journal of Remote Sensing
A novel pixon-representation for image segmentation based on Markov random field
Image and Vision Computing
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Genetic algorithms for video segmentation
Pattern Recognition
Automatic object-based video segmentation using distributed genetic algorithms
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
Unsupervised texture segmentation using multiple segmenters strategy
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Unsupervised detection of mammogram regions of interest
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Illumination invariant unsupervised segmenter
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Crack detection in X-ray images using fuzzy index measure
Applied Soft Computing
Colour texture segmentation using modelling approach
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Performance evaluation of a segmentation algorithm for synthetic texture images
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Fusion of edge information in markov random fields region growing image segmentation
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Image segmentation using a genetic algorithm and hierarchical local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
ESVC-based extraction and segmentation of texture features
Computers & Geosciences
Moving object detection using Markov Random Field and Distributed Differential Evolution
Applied Soft Computing
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Among the existing texture segmentation methods, those relying on Markov random fields have retained substantial interest and have proved to be very efficient in supervised mode. The use of Markov random fields in unsupervised mode is, however, hampered by the parameter estimation problem. The recent solutions proposed to overcome this difficulty rely on assumptions about the shapes of the textured regions or about the number of textures in the input image that may not be satisfied in practice. In this paper, an evolutionary approach, selectionist relaxation, is proposed as a solution to the problem of segmenting Markov random field modeled textures in unsupervised mode. In selectionist relaxation, the computation is distributed among a population of units that iteratively evolves according to simple and local evolutionary rules. A unit is an association between a label and a texture parameter vector. The units whose likelihood is high are allowed to spread over the image and to replace the units that receive lower support from the data. Consequently, some labels are growing while others are eliminated. Starting with an initial random population, this evolutionary process eventually results in a stable labelization of the image, which is taken as the segmentation. In this work, the generalized Ising model is used to represent textured data. Because of the awkward nature of the partition function in this model, a high-temperature approximation is introduced to allow the evaluation of unit likelihoods. Experimental results on images containing various synthetic and natural textures are reported.