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

作者：Hegenbart Sebastian^*; Uhl Andreas

来源：Pattern Recognition, 2015, 48(8): 2633-2644.

DOI：10.1016/j.patcog.2015.02.024

• LBP
• Texture
• Classification
• Scale
• Adaptive
• Rotation
• Invariant
• Scale-space

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.

• 出版日期2015-8