International Journal of Computer Vision
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
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Discriminative Feature Co-Occurrence Selection for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Feature Extraction Using Gradient Local Auto-Correlations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Rotation invariant co-occurrence among adjacent LBPs
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
Indirect immunofluorescence image classification using texture descriptors
Expert Systems with Applications: An International Journal
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In this paper, we propose a new image feature based on spatial co-occurrence among micropatterns, where each micropattern is represented by a Local Binary Pattern (LBP). In conventional LBP-based features such as LBP histograms, all the LBPs of micropatterns in the image are packed into a single histogram. Doing so discards important information concerning spatial relations among the LBPs, even though they may contain information about the image's global structure. To consider such spatial relations, we measure their co-occurrence among multiple LBPs. The proposed feature is robust against variations in illumination, a feature inherited from the original LBP, and simultaneously retains more detail of image. The significant advantage of the proposed method versus conventional LBP-based features is demonstrated through experimental results of face and texture recognition using public databases.