Image description using joint distribution of filter bank responses
Pattern Recognition Letters
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Radon representation-based feature descriptor for texture classification
IEEE Transactions on Image Processing
A realistic benchmark for visual indoor place recognition
Robotics and Autonomous Systems
Computer Vision and Image Understanding
Image and Vision Computing
Using Basic Image Features for Texture Classification
International Journal of Computer Vision
Colour and rotation invariant textural features based on Markov random fields
Pattern Recognition Letters
Descriptor learning based on fisher separation criterion for texture classification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
Material detection based on fractal approach
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
Extended local binary patterns for texture classification
Image and Vision Computing
Extending morphological covariance
Pattern Recognition
Comparative study of moment based parameterization for morphological texture description
Journal of Visual Communication and Image Representation
Continuous rotation invariant local descriptors for texton dictionary-based texture classification
Computer Vision and Image Understanding
Texture recognition using robust Markovian features
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Visual recognition using local quantized patterns
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Local higher-order statistics (LHS) for texture categorization and facial analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Recognizing materials from virtual examples
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Gated boltzmann machine in texture modeling
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Texture classification based on BIMF monogenic signals
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Texture databases - A comprehensive survey
Pattern Recognition Letters
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Although a considerable amount of work has been published on material classification, relatively little of it studies situations with considerable variation within each class. Many experiments use the exact same sample, or different patches from the same image, for training and test sets. Thus, such studies are vulnerable to effectively recognising one particular sample of a material as opposed to the material category. In contrast, this paper places firm emphasis on the capability to generalise to previously unseen instances of materials. We adopt an appearance-based strategy, and conduct experiments on a new database which contains several samples of each of eleven material categories, imaged under a variety of pose, illumination and scale conditions. Together, these sources of intra-class variation provide a stern challenge indeed for recognition. Somewhat surprisingly, the difference in performance between various state-of-the-art texture descriptors proves rather small in this task. On the other hand, we clearly demonstrate that very significant gains can be achieved via different SVM-based classification techniques. Selecting appropriate kernel parameters proves crucial. This motivates a novel recognition scheme based on a decision tree. Each node contains an SVM to split one class from all others with a kernel parameter optimal for that particular node. Hence, each decision is made using a different, optimal, class-specific metric. Experiments show the superiority of this approach over several state-of-the-art classifiers.