Abstract
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.
Highlights
In recent years, image feature description methods have received significant attention in the fields of computer vision and pattern recognition
A number of image feature extraction methods are proposed, which can be divided into two categories: holistic and local image feature extraction
Since local binary pattern (LBP) is a two-value model, which cannot describe more detailed information, Tan [25] extends the two-value model to the three-value model and proposes a novel local feature description method, local ternary patterns (LTP)
Summary
Image feature description methods have received significant attention in the fields of computer vision and pattern recognition. Symmetry 2016, 8, 109 description method called the local binary pattern (LBP), which can achieve superior results for image recognition. R. Since LBP is a two-value model, which cannot describe more detailed information, Tan [25] extends the two-value model to the three-value model and proposes a novel local feature description method, local ternary patterns (LTP). Motivated by the LBP structure and Gabor filters, we propose a novel texture feature description method based on GGDP and WDMM. LBP cannot obtain more detailed direction information from other neighborhood pixels, and is sensitive to noise To overcome these defects, we propose a novel patch-structure direction pattern (PDP). To further improve the effectiveness of PDP, we introduce it into multi-channel Gabor space and get an improved method called GGDP, which can better describe multi-direction and multi-scale texture information.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
