Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform

Yang Zhihua; Zhang Qian; Yang Lihua

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Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform

Yang Zhihua 1, Zhang Qian 2,Yang Lihua 3

1. Information Science School, Guangdong Finance andEconomics University, Guangzhou 510320

2. College of Mathematics and Computional Science, Shenzhen University, Shenzhen 518060

3. School of Mathematics, Sun Yat-sen University, Guangzhou 510275

*Correspondence author

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Funding: Research Fund for the Doctoral Program of Higher Education of China （No.20130171110016）

Opened online:28 April 2017

Accepted by: none

Citation: Yang Zhihua, Zhang Qian,Yang Lihua.Illumination and Rotation Invariant Featureof Texture Images Based on Hilbert-Huang Transform[OL]. [28 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726832

This paper presents a novel method to extract theillumination and rotation invariant features for texture imagesbased on Hilbert-Huang transform. Texture images are usually ofquasi-periodic. It is shown in this paper that the main frequencyof the Hilbert marginal spectrum of a texture image can be used to measure the approximate period effectively and thus can be servedas a good feature for texture classification. This feature isproved to be invariant to uneven illumination. Being modified, itis shown that this feature is also invariant rotation. Experimentshave been conducted to compare the feature with the existing ones.It is shown that the proposed approach outperforms the existingmethods in both recognition rate and robustness to unevenillumination, rotation and noise pollution.

Keywords:Empirical mode decomposition (EMD), Hilbert-Huangtransform (HHT), Main frequency center, Texture Classification

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