Experiments in colour texture analysis
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for texture classification using the coordinated clusters representation
Pattern Recognition Letters
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Colour Image Texture Analysis: Dependence on Colour Spaces
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Color Calibration for a Dermatological Video Camera System
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Comparative experiment with colour texture classifiers using the CCR feature space
Pattern Recognition Letters
Generalized Co-Occurrence Matrix for Multispectral Texture Analysis
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
A multiscale representation including opponent color features for texture recognition
IEEE Transactions on Image Processing
Perceptually uniform color spaces for color texture analysis: an empirical evaluation
IEEE Transactions on Image Processing
Color texture analysis based on fractal descriptors
Pattern Recognition
Performance analysis of colour descriptors for parquet sorting
Expert Systems with Applications: An International Journal
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
Color texture classification based on gravitational collapse
Pattern Recognition
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The Coordinated Clusters Representation (CCR) is a texture descriptor based on the probability of occurrence of elementary binary patterns (texels) defined over a square window. The CCR was originally proposed for binary textures, and it was later extended to grayscale texture images through global image thresholding. The required global binarization is a critical point of the method, since this preprocessing stage can wipe out textural information. Another important drawback of the original CCR model is its sensitivity against rotation. In this paper we present a rotation-invariant CCR-based model for colour textures which yields a twofold improvement over the grayscale CCR: first, the use of rotation-invariant texels makes the model insensitive against rotation; secondly, the new texture model benefits from colour information and does not need global thresholding. The basic idea of the method is to describe the textural and colour content of an image by splitting the original colour image into a stack of binary images, each one representing a colour of a predefined palette. The binary layers are characterized by the probability of occurrence of rotation-invariant texels, and the overall feature vector is obtained by concatenating the histograms computed for each layer. In order to quantitatively assess our approach, we performed experiments over two datasets of colour texture images using five different colour spaces. The obtained results show robust invariance against rotation and a marked increase in classification accuracy with respect to grayscale versions of CCR and LBP.