Book Chapter10.1007/978-3-319-71607-7_18
Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classification
You Hao,Shirui Li,Hanlin Mo,Hua Li +3 more
- 13 Sep 2017
- pp 199-210
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TL;DR: In this paper, an Affine-Gradient based Local Binary Pattern (AGLBP) descriptor was proposed for texture classification. But it is difficult to describe complicated texture using single type information, such as Local Binary Patterns (LBP), which just utilizes the sign information of the difference between pixel and its local neighbors.
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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 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.
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Citations
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A Completed Multiply Threshold Encoding Pattern for Texture Classification
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
TL;DR: It is shown that CLBP_S preserves more information of the local structure thanCLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well and can be made for rotation invariant texture classification.