Noise robust rotation invariant features for texture classification
Rouzbeh Maania✉, Sanjay Kalrab c, Yee‐Hong Yanga
- aDepartment of Computing Science, University of Alberta, Canada AB T6G 2E8
- bDepartment of Biomedical Engineering, University of Alberta, Canada
- cDepartment of Medicine, University of Alberta, Canada
- ✉Corresponding author
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Abstract
This paper presents a novel, simple, yet powerful and robust method for rotation invariant texture classification. Like the Local Binary Patterns (LBP), the proposed method considers at each pixel a neighboring function defined on a circle of radius R. We define local frequency components as the magnitude of the coefficients of the 1D Fourier transform of the neighboring function. By applying different bandpass filters on the 2D Fourier transform of the local frequency components, we define our Local Frequency Descriptors (LFD). The LFD features are added dynamically from low frequencies to high. The features defined in this paper are invariant to rotation. As well, they are robust to noise. The experimental results on the Outex, CUReT, and KTH-TIPS datasets show that the proposed method outperforms state-of-the-art texture analysis methods. The results also show that the proposed method is very robust to noise.
TL;DRA novel, simple, yet powerful and robust method for rotation invariant texture classification, which outperforms state-of-the-art texture analysis methods and is robust to noise. (Semantic Scholar)