A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Texture Modeling by Multiple Pairwise Pixel Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Pseudo-Metric for Weighted Point Sets
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Optimal Mass Transport for Registration and Warping
International Journal of Computer Vision
Texture Based Segmentation: Automatic Selection of Co-Occurrence Matrices
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Editorial: Special Issue on "Texture Analysis and Synthesi"
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Image analysis with local binary patterns
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
An efficient and effective region-based image retrieval framework
IEEE Transactions on Image Processing
Local greylevel appearance histogram based texture segmentation
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
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The ability of human observers to discriminate between textures is related to the contrast between key structural elements and their repeating patterns. Here we have developed an automatic texture classification approach based on this principle. Local contrast information is modelled and a hybrid metric, based on probability density distributions and transportation estimation, are used to classify unseen samples. Quantitative and qualitative evaluation, based on mammographic images and Wolfe classification, is presented and shows segmentation results in line with the various classes.