Title: Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication,
Abstract: We present a novel Affine-Gradient based Local Binary Pattern (AGLBP)
descriptor for texture classification. It is very hard to describe complicated
texture using single type information, such as Local Binary Pattern (LBP),
which just utilizes the sign information of the difference between the pixel
and its local neighbors. Our descriptor has three characteristics: 1) In order
to make full use of the information contained in the texture, the
Affine-Gradient, which is different from Euclidean-Gradient and invariant to
affine transformation is incorporated into AGLBP. 2) An improved method is
proposed for rotation invariance, which depends on the reference direction
calculating respect to local neighbors. 3) Feature selection method,
considering both the statistical frequency and the intraclass variance of the
training dataset, is also applied to reduce the dimensionality of descriptors.
Experiments on three standard texture datasets, Outex12, Outex10 and KTH-TIPS2,
are conducted to evaluate the performance of AGLBP. The results show that our
proposed descriptor gets better performance comparing to some state-of-the-art
rotation texture descriptors in texture classification.