A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics (TOG)
Filtering for Texture Classification: A Comparative Study
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
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
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Markov random field texture models
Markov random field texture models
An introduction to variable and feature selection
The Journal of Machine Learning Research
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
Class-Specific Material Categorisation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
A Statistical Approach to Material Classification Using Image Patch Exemplars
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Texture analysis based on saddle points-based BEMD and LBP
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Discriminative features for texture description
Pattern Recognition
Per-patch descriptor selection using surface and scene properties
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Noise robust rotation invariant features for texture classification
Pattern Recognition
Texture classification based on BIMF monogenic signals
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Learning discriminant face descriptor for face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fisher separation criteria (FSC) to learn most reliable and robust dominant pattern types considering intraclass similarity and inter-class distance. Image structures are thus be described by a new FSC-based learning (FBL) encoding method. Unlike previous handcraft-design encoding methods, such as the LBP and SIFT, supervised learning approach is used to learn an encoder from training samples. We find that such a learning technique can largely improve the discriminative ability and automatically achieve a good tradeoff between discriminative power and efficiency. The commonly used texture descriptor: local binary pattern (LBP) is taken as an example in the paper, so that we then proposed the FBL-LBP descriptor. We benchmark its performance by classifying textures present in the Outex_TC_0012 database for rotation invariant texture classification, KTH-TIPS2 database for material categorization and Columbia-Utrecht (CUReT) database for classification under different views and illuminations. The promising results verify its robustness to image rotation, illumination changes and noise. Furthermore, to validate the generalization to other problems, we extend the application also to face recognition and evaluate the proposed FBL descriptor on the FERET face database. The inspiring results show that this descriptor is highly discriminative.