Texture classification using optimized local ternary patterns with nonlinear diffusion as pre-processing

G. Madasamy Raja

doi:10.1007/s11042-019-7197-0

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Texture classification using optimized local ternary patterns with nonlinear diffusion as pre-processing

2019-01-25

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DOI：10.1007/s11042-019-7197-0

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Abstract

En 中文

The main focus of this paper is to improve the performance of the texture model Optimized Local Ternary Patterns (OLTP), which is known as one of the successful variants of the texture model Local Binary Patterns (LBP), a well known method for texture analysis and its applications. Generally preprocessing is used in digital image processing for reducing the unwanted noise and disturbances in such a way that it improves the quality of the image. Preprocessing not only removes the distortions but also enhances the features of the image for further processing. To achieve better recognition accuracy, the texture model OLTP was combined with a preprocessing method that uses nonlinear diffusion method as a preprocessing tool in this paper, with the hope that this idea will surely improve the local feature description and texture classification process. This nonlinear diffusion method uses two newly developed edge stopping functions for preprocessing. This proposed method is tested with two standard texture datasets namely Brodatz Dataset and Usptex dataset. The results show that the use of the preprocessing step really improved the texture classification accuracy.

Keywords:

Texture classification

Optimized local ternary patterns (OLTP)

Texture dataset

Nonlinear anisotropic diffusion

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