Rotation and scale invariant local binary pattern based on high order directional derivatives for texture classification

Authors:
Feiniu Yuan
Affiliations:
-
Venue:
Digital Signal Processing
Year:
2014

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Abstract

Local Binary Pattern (LBP) only encodes the first order directional derivatives of a center pixel but it does not consider higher order derivatives. This paper proposes a rotation and scale invariant local binary pattern by jointly taking into account high order directional derivatives, circular shift sub-uniform, and scale space. Each order directional derivatives are independently encoded in a similar way of the first order derivatives to generate a code for the center pixel. Different order derivatives produce different codes that result in several histograms over an image, and then all the histograms multiplied by weights are concatenated together to fully utilize information of different order derivatives. To further improve performance, circular shift sub-uniform and scale space techniques are used to obtain rotation and scale invariant local binary patterns. Extensive experiments show that the high order derivatives based LBP can achieve good performance and obviously outperforms existing methods.