A scale- and orientation-adaptive extension of Local Binary Patterns for texture classification

PATTERN RECOGNITION

卷:48

Article

Hegenbart, Sebastian¹  Uhl, Andreas¹ 

[1] Salzburg Univ, Dept Comp Sci, A-5020 Salzburg, Austria

关键词: LBP;    Texture;    Classification;    Scale;    Adaptive;    Rotation;    Invariant;    Scale-space;   

DOI  :  10.1016/j.patcog.2015.02.024

来源: Elsevier

【 摘 要 】

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. (C) 2015 The Authors. Published by Elsevier Ltd.

【 授权许可】

Free