Texture target classification with CLBP and local geometric features

Kou Qiqi; Cheng Deqiang; Yu Wenjie; Li Huayu

doi:10.12086/oee.2019.180604

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Kou Qiqi, Cheng Deqiang, Yu Wenjie, et al. Texture target classification with CLBP and local geometric features[J]. Opto-Electronic Engineering, 2019, 46(11): 180604. doi: 10.12086/oee.2019.180604

Citation:

Kou Qiqi, Cheng Deqiang, Yu Wenjie, et al. Texture target classification with CLBP and local geometric features[J]. Opto-Electronic Engineering, 2019, 46(11): 180604. doi: 10.12086/oee.2019.180604

Texture target classification with CLBP and local geometric features

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
2.
Anhui Province Wanbei Coal and Electricity Group Co., Ltd. Information Center, Suzhou, Anhui 234000, China

Fund Project: Supported by National Natural Science Foundation of China (51774281) and Xuzhou Science and Technology Project (KC16ZI214)

More Information

Corresponding author: Cheng Deqiang, E-mail:deqcheng@163.com

Received Date 21 November 2018

Revised Date 24 April 2019

Published Date 01 November 2019

Abstract

Abstract

For the problems of needing pre-training and poor robustness to rotation and illumination changes of various improved algorithms based on local binary pattern (LBP), this paper presents a new texture classification algorithm by integrating the completed local binary pattern (CLBP) and the local geometric invariant features of the image surface. In our algorithm, the local geometric invariant features are first computed. Then the computed results are further quantified and encoded to make combination with the CLBP histogram. The proposed algorithm can extract image macroscopic and microscopic features simultaneously, and it has the properties of not significantly increasing feature dimension, without pre-training, and invariance to the rotation and illumination changes. Experimental verifications are conducted on two standard texture databases, and the results demonstrate that the proposed algorithm outperforms the comparative classification algorithms in classification accuracy and robustness.
- LBP /
- CLBP /
- texture classification /
- local geometric invariant features

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References

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Overview

Overview

Overview: As an important approach for describing and identifying targets, texture plays an important role in image processing, pattern recognition and machine vision. Textures vary in rotation, illumination, and slight viewpoint variations as imaging conditions change, classification algorithm using local binary pattern (LBP) can achieve prominent effectiveness. However, the selected rotation invariant structural patterns in LBP and many LBP variants are discrete and have poor robustness to rotation changes. Furthermore, the feature information extracted from many algorithms is complex and redundant, resulting in a high computational cost and feature dimensionality. A preeminent texture feature not only requires superior identification ability and robustness, but also has the characteristics of low computational complexity and feature dimension. Local geometric invariant feature can keep invariance under the condition of changing the impact factors such as rotation, scaling, viewpoint transformation and illumination. Furthermore, texture classification algorithm using local geometric invariant feature can achieve remarkable effectiveness in the condition of non-rigid deformation, shelter, noise and other influencing factors. Therefore, local geometric invariant feature has been used in many fields of computer vision, such as wide baseline image matching, panorama splicing, target recognition, image retrieval and scene reconstruction. To make the texture classification algorithm robust, local geometric invariant feature of target is usually extracted as its characteristic descriptor. For the problems of needing pre-training and poor robustness to rotation and illumination changes of various improved algorithms based on local binary pattern (LBP), this paper presents a new texture classification algorithm by integrating the completed local binary pattern (CLBP) and the local geometric invariant features of the image surface. In our algorithm, the local geometric invariant features are first computed and quantified. Then the quantified results are coded to keep consistent with the coding scheme of CLBP. Finally, the local geometric invariant feature histogram is made concatenation with the histogram of CLBP. Since we use the principal curvatures of a point on an image surface as the local geometric invariant feature, both micro- and macro-structure texture information can also be captured simultaneously. Moreover, the principal curvature has the property of continuous rotation invariance. Therefore, the proposed algorithm can extract the macroscopic and microscopic features of the image at the same time, and it has the properties of moderate feature dimension, without pre-training, and invariance to the rotation and illumination changes. Experimental verifications are conducted on two standard texture databases, and the results demonstrates that the proposed algorithm outperforms the comparative classification algorithms in classification accuracy and robustness.