Unsupervised texture segmentation using Gabor filters
Pattern Recognition
The Complexity of Multiterminal Cuts
SIAM Journal on Computing
Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks for pattern recognition
Neural networks for pattern recognition
Making large-scale support vector machine learning practical
Advances in kernel methods
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Color Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Retrieval
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Multivariate mixtures of normals with unknown number of components
Statistics and Computing
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
A new graph cut-based multiple active contour algorithm without initial contours and seed points
Machine Vision and Applications
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
Hypergraph Cuts & Unsupervised Representation for Image Segmentation
Fundamenta Informaticae
Image segmentation algorithms based on the machine learning of features
Pattern Recognition Letters
Color-texture image segmentation and recognition through a biologically-inspired architecture
Pattern Recognition and Image Analysis
A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features
Image Communication
Texture aware image segmentation using graph cuts and active contours
Pattern Recognition
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This paper proposes a novel approach to color-texture segmentation based on graph cut techniques, which finds an optimal color-texture segmentation of a color textured image by regarding it as a minimum cut problem in a weighted graph. A new texture descriptor based on the texton theory is introduced to efficiently represent texture attributes of the given image. Then, the segmentation is formulated in terms of energy minimization with graph cuts, where color and texton features are modelled with a multivariate finite mixture model with an unknown number of components. Contrary to previous supervised graph cut approaches, our method finds minimum cuts using split moves in an unsupervised way. The segmentation result, including the number of segments, is determined during the split moves without user interaction. Thus, our method is called unsupervised graph cuts. Experimental results of color-texture segmentation using various images including the MIT VisTex datasets and the Berkeley datasets are presented and analyzed in terms of precision and recall to verify its effectiveness.