Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication
This work addresses texture classification, a domain-specific problem in computer vision, with incremental improvements over existing descriptors.
The authors tackled texture classification by proposing the Affine-Gradient based Local Binary Pattern (AGLBP) descriptor, which incorporates affine-invariant gradients and rotation invariance, achieving better performance than state-of-the-art methods on standard datasets like Outex12, Outex10, and KTH-TIPS2.
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.