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
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
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
Multi-scale binary patterns for texture analysis
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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
Learning local features for age estimation on real-life faces
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Computer Vision Using Local Binary Patterns
Computer Vision Using Local Binary Patterns
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Learning multi-scale block local binary patterns for face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Indirect immunofluorescence image classification using texture descriptors
Expert Systems with Applications: An International Journal
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This work presents a novel image appearance description method based on the highly popular local binary pattern (LBP) texture features. The key idea consists of introducing a dense sampling encoding strategy for extracting more stable and discriminative texture patterns in local regions. Compared to the conventional "sparse" sampling scheme commonly used in basic LBP, our proposed dense sampling aims to generate, through a form of up-sampling, more neighboring pixels so that more stable LBP codes, carrying out richer information, are computed. This yields in significantly enhanced image description which is less prone to noise and to sparse and unstable histograms. Another interesting property of the dense sampling scheme is that it can be easily integrated with many existing LBP variants. Extensive experiments on three different classification problems namely face recognition, texture classification and age group estimation on various challenging benchmark databases clearly demonstrate the efficiency of the proposed scheme, showing very promising results compared not only to original LBP but also to state-of-the-art especially in the very demanding task of human age estimation.