Scale and pattern adaptive local binary pattern for texture classification[Formula presented]
文献类型:期刊论文
作者 | Hu, Shiqi3,4; Li, Jie4; Fan, Hongcheng2; Lan, Shaokun4; Pan, Zhibin1,4 |
刊名 | Expert Systems with Applications |
出版日期 | 2024-04-15 |
卷号 | 240 |
ISSN号 | 09574174 |
关键词 | Local binary pattern (LBP) Texture classification Low dimension Scale and pattern adaptive selection Kirsch operator |
DOI | 10.1016/j.eswa.2023.122403 |
产权排序 | 4 |
英文摘要 | Local binary pattern (LBP) with a fixed sampling template is sensitive to scale changes. Furthermore, under rotation changes or noise corruptions, one uniform LBP pattern can be corrupted to fall into a non-uniform pattern which loses its discrimination power to describe the corresponding texture feature. To overcome these two main drawbacks, we propose a scale and pattern adaptive local binary pattern (SPALBP). Firstly, in the gradient-based sampling radius adaptive scheme, eight directional adaptive sampling radius of each center pixel can be obtained by using its eight Kirsch gradient values. Secondly, in the noise and rotation robust neighborhood sampling scheme, three neighborhood sampling templates are used to extract three kinds of averaging neighborhood pixels. Thirdly, for each center pixel, three kinds of LBPriu2 patterns can be extracted by sampling these three kinds of averaging neighborhood pixels along eight directional adaptive sampling radius. Finally, an optimal SPALBP uniform pattern can be adaptively selected from these three LBPriu2 patterns. Hence, all SPALBP patterns show more robustness against scale changes, rotation changes and noise corruptions. Extensive experiments are conducted on four standard texture databases: Outex, UIUC, CUReT and XU_HR. Comparing with state-of-the-art LBP-based variants, the proposed SPALBP method consistently shows superior performance both in dramatic environment changes and high-levels of noise conditions, meanwhile it maintains a lower texture feature dimension. © 2023 Elsevier Ltd |
语种 | 英语 |
出版者 | Elsevier Ltd |
源URL | [http://ir.opt.ac.cn/handle/181661/97129] |
专题 | 西安光学精密机械研究所_瞬态光学技术国家重点实验室 |
通讯作者 | Pan, Zhibin |
作者单位 | 1.State Key Laboratory of Transient Optics and Photonics, Chinese Academy of Sciences, Xian; 710119, China 2.The Institute of Information and Navigation, Air Force Engineering University, Xi'an; 710077, China; 3.The AVIC Xi'an Flight Automatic Control Research Institute, Xi'an; 710076, China; 4.Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an; 710049, China; |
推荐引用方式 GB/T 7714 | Hu, Shiqi,Li, Jie,Fan, Hongcheng,et al. Scale and pattern adaptive local binary pattern for texture classification[Formula presented][J]. Expert Systems with Applications,2024,240. |
APA | Hu, Shiqi,Li, Jie,Fan, Hongcheng,Lan, Shaokun,&Pan, Zhibin.(2024).Scale and pattern adaptive local binary pattern for texture classification[Formula presented].Expert Systems with Applications,240. |
MLA | Hu, Shiqi,et al."Scale and pattern adaptive local binary pattern for texture classification[Formula presented]".Expert Systems with Applications 240(2024). |
入库方式: OAI收割
来源:西安光学精密机械研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。