Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Covariance Tracking using Model Update Based on Lie Algebra
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gabor Filters as Feature Images for Covariance Matrix on Texture Classification Problem
Advances in Neuro-Information Processing
SIFT Flow: Dense Correspondence across Scenes and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor-LBP Based Region Covariance Descriptor for Person Re-identification
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor-Based Region Covariance Matrices for Face Recognition
IEEE Transactions on Circuits and Systems for Video Technology
Local log-euclidean covariance matrix (L2ECM) for image representation and its applications
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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This paper presents a novel way to embed local binary texture information in the form of local binary patterns (LBP) into the covariance descriptor. Contrary to previous publications, our method is not based on the LBP decimal values where arithmetic operations have no texture meaning. Our method uses the angles described by the uniform LBP patterns and includes them into the set of features used to build the covariance descriptor. Our representation is not only more compact but more robust because it is less affected by noise and small neighborhood rotations. Experimental evaluations corroborate the performance of our descriptor for texture analysis and tracking applications. Our descriptor rivals with state-of-the-art methods and beats other covariance-based descriptors.