A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian MRF Rotation-Invariant Features for Image Classification
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
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Learning dictionaries of stable autoregressive models for audio scene analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Statistical Approach to Material Classification Using Image Patch Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
An efficient algorithm for a class of fused lasso problems
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Enhanced Shift and Scale Tolerance for Rotation Invariant Polar Matching With Dual-Tree Wavelets
IEEE Transactions on Image Processing
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Anisotropic, rotation invariant, autoregressive random fields, realised by considering local radial sampling, are flexible models which have been considered for texture classification. Unfortunately, owing to the strong correlations present in the neighbourhood covariate matrix, parameter estimation is complicated by the dichotomy between ill-conditionedness and rotation invariance. Exploiting the Fused Lasso framework, we here propose a compromise which incorporates two regularisers. The @?"1-norm induces stability and performs variable selection amongst strongly correlated radial samples; the total variation seminorm encourages clustering and promotes parsimony. Experiments confirm the potential utility. Parallels are drawn within the texture classification literature and beyond.