A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model
Texture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Symmetry |
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| Online Access: | http://www.mdpi.com/2073-8994/8/11/109 |
| _version_ | 1811183794946834432 |
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| author | Ting Chen Xiangmo Zhao Liang Dai Licheng Zhang Jiarui Wang |
| author_facet | Ting Chen Xiangmo Zhao Liang Dai Licheng Zhang Jiarui Wang |
| author_sort | Ting Chen |
| collection | DOAJ |
| description | Texture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose a novel method based on generalized Gabor direction pattern (GGDP) and weighted discrepancy measurement model (WDMM) to overcome those defects. Firstly, a novel patch-structure direction pattern (PDP) is proposed, which can extract rich feature information and be insensitive to noise. Then, motivated by searching for a description method that can explore richer and more discriminant texture features and reducing the local Gabor feature vector’s high dimension problem, we extend PDP to form the GGDP method with multi-channel Gabor space. Furthermore, WDMM, which can effectively measure the feature distance between two images, is presented for the classification and recognition of image samples. Simulated experiments on olivetti research laboratory (ORL), Carnegie Mellon University pose, illumination, and expression (CMUPIE) and Yale B face databases under different illumination or facial expression conditions indicate that the proposed method outperforms other existing classical methods. |
| first_indexed | 2024-04-11T11:52:15Z |
| format | Article |
| id | doaj.art-a32216ac28ec4fe39b38b9981859fd76 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T11:52:15Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-a32216ac28ec4fe39b38b9981859fd762022-12-22T04:25:17ZengMDPI AGSymmetry2073-89942016-10-0181110910.3390/sym8110109sym8110109A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement ModelTing Chen0Xiangmo Zhao1Liang Dai2Licheng Zhang3Jiarui Wang4School of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaTexture feature description is a remarkable challenge in the fields of computer vision and pattern recognition. Since the traditional texture feature description method, the local binary pattern (LBP), is unable to acquire more detailed direction information and always sensitive to noise, we propose a novel method based on generalized Gabor direction pattern (GGDP) and weighted discrepancy measurement model (WDMM) to overcome those defects. Firstly, a novel patch-structure direction pattern (PDP) is proposed, which can extract rich feature information and be insensitive to noise. Then, motivated by searching for a description method that can explore richer and more discriminant texture features and reducing the local Gabor feature vector’s high dimension problem, we extend PDP to form the GGDP method with multi-channel Gabor space. Furthermore, WDMM, which can effectively measure the feature distance between two images, is presented for the classification and recognition of image samples. Simulated experiments on olivetti research laboratory (ORL), Carnegie Mellon University pose, illumination, and expression (CMUPIE) and Yale B face databases under different illumination or facial expression conditions indicate that the proposed method outperforms other existing classical methods.http://www.mdpi.com/2073-8994/8/11/109face recognitiontexture feature descriptionfeature extractionLBPlocal Gabor transform |
| spellingShingle | Ting Chen Xiangmo Zhao Liang Dai Licheng Zhang Jiarui Wang A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model Symmetry face recognition texture feature description feature extraction LBP local Gabor transform |
| title | A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model |
| title_full | A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model |
| title_fullStr | A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model |
| title_full_unstemmed | A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model |
| title_short | A Novel Texture Feature Description Method Based on the Generalized Gabor Direction Pattern and Weighted Discrepancy Measurement Model |
| title_sort | novel texture feature description method based on the generalized gabor direction pattern and weighted discrepancy measurement model |
| topic | face recognition texture feature description feature extraction LBP local Gabor transform |
| url | http://www.mdpi.com/2073-8994/8/11/109 |
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