An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
RegionBoost learning for 2D+3D based face recognition
Pattern Recognition Letters
A reliable method for cell phenotype image classification
Artificial Intelligence in Medicine
Local binary patterns for a hybrid fingerprint matcher
Pattern Recognition
A novel extended local-binary-pattern operator for texture analysis
Information Sciences: an International Journal
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Fuzzy Local Binary Patterns for Ultrasound Texture Characterization
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Thyroid Texture Representation via Noise Resistant Image Features
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Centralized Binary Patterns Embedded with Image Euclidean Distance for Facial Expression Recognition
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
Plant leaf identification using Gabor wavelets
International Journal of Imaging Systems and Technology
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Computers in Biology and Medicine
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image retrieval based on multi-texton histogram
Pattern Recognition
Enhanced local texture feature sets for face recognition under difficult lighting conditions
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
False positive reduction in mammographic mass detection using local binary patterns
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Co-training with relevant random subspaces
Neurocomputing
Local binary patterns variants as texture descriptors for medical image analysis
Artificial Intelligence in Medicine
A completed modeling of local binary pattern operator for texture classification
IEEE Transactions on Image Processing
Automatic classification of granite tiles through colour and texture features
Expert Systems with Applications: An International Journal
Texture Description Through Histograms of Equivalent Patterns
Journal of Mathematical Imaging and Vision
Three-fold structured classifier design based on matrix pattern
Pattern Recognition
Matrix based cyclic spectral estimator for fast and robust texture classification
The Visual Computer: International Journal of Computer Graphics
Hi-index | 12.05 |
The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases.