title: A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification.
abstract: Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformations. In this work, we present a scale- and rotation-invariant computation of LBP. Rotation-invariance is achieved by explicit alignment of features at the extraction level, using a robust estimate of global orientation. Scale-adapted features are computed in reference to the estimated scale of an image, based on the distribution of scale normalized Laplacian responses in a scale-space representation. Intrinsic-scale-adaption is performed to compute features, independent of the intrinsic texture scale, leading to a significantly increased discriminative power for a large amount of texture classes. In a final step, the rotation- and scale-invariant features are combined in a multi-resolution representation, which improves the classification accuracy in texture classification scenarios with scaling and rotation significantly.
authors.affiliation: Department of Computer Sciences, University of Salzburg, Jakob-Haringer Strasse 2, 5020 Salzburg, Austria.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Hegenbart
authors.affiliation: Department of Computer Sciences, University of Salzburg, Jakob-Haringer Strasse 2, 5020 Salzburg, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Uhl
keywords: Adaptive; Classification; Invariant; LBP; Rotation; Scale; Scale-space; Texture