Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Material Classification Using Image Patch Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Texture Analysis and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification from Random Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sorted random projections for robust rotation-invariant texture classification
Pattern Recognition
Scale invariant texture analysis using multi-scale local autocorrelation features
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Discriminative features for texture description
Pattern Recognition
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Texture Classification Using Dominant Neighborhood Structure
IEEE Transactions on Image Processing
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Noise robust rotation invariant features for texture classification
Pattern Recognition
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Scale change exists very commonly in real-world textural images which remains one of the biggest challenges in texture classification due to the tremendous changes involved in texture appearance. While most research efforts have been devoted to extracting various scale invariant features, these methods are either unsuitable to describe a texture or unable to handle the situations where a large amount of scale change exists. Other works attempt to avoid scale invariant feature extraction by generating a set of multi-scale representations from training images for classification, but they are not only computation intensive but also limited to dealing with small scale changes between training images and test images. In this paper we investigate the scaling properties of textures and introduce a low dimensional linear subspace for the multi-scale representations of a texture, in which the collaboration between the multi-scale representations is beneficial for the scale invariant texture classification. We therefore propose a new scale invariant texture classification framework without extracting scale invariant features, by using a sparse representation technique to model the multi-scale representations of a texture and taking the advantages of collaboration between them for classification. Specifically, a multi-scale dictionary is constructed from the Gaussian-pyramid-generated scale space of a small set of training images at one scale, and then the test images at arbitrary scales are classified via a modified sparse representation based classification method. Experiments on two benchmark texture databases show that the proposed method is able to deal with large scale changes between the training images and the test images and achieve comparative results to the state-of-the-art approaches for the classification of textures with various variations, especially scale.