An approach to reducing the labeling cost of Markov random fields within an infinite label space
Signal Processing - Special section on digital signal processing for multimedia communications and services
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of complementary DNA microarray images by wavelet-based Markov random field model
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Double Markov random fields and Bayesian image segmentation
IEEE Transactions on Signal Processing
Image segmentation and similarity of color-texture objects
IEEE Transactions on Multimedia
Joint scene classification and segmentation based on hidden Markov model
IEEE Transactions on Multimedia
IEEE Transactions on Fuzzy Systems
Sonar image segmentation using an unsupervised hierarchical MRF model
IEEE Transactions on Image Processing
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Image segmentation using a texture gradient based watershed transform
IEEE Transactions on Image Processing
Adaptive scale fixing for multiscale texture segmentation
IEEE Transactions on Image Processing
Morphology-based multifractal estimation for texture segmentation
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
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An unsupervised multiresolution conditional random field (CRF) approach to texture segmentation problems is introduced. This approach involves local and long-range information in the CRF neighbourhood to determine the classes of image blocks. Likemost Markov random field (MRF) approaches, the proposed method treats the image as an array of random variables and attempts to assign an optimal class label to each. While most MRFs involve only local information extracted from a small neighbourhood, our method also allows a few long-range blocks to be involved in the labelling process. This alleviates the problem of assigning different class labels to disjoint regions of the same texture and oversegmentation due to the lack of long-range interaction among the neighbouring and distant blocks. The proposed method requires no a priori knowledge of the number and types of regions/textures.