Luo, Q., Su, J., Yang, C., Silven, O., & Liu, L. (2022). Scale-selective and noise-robust extended local binary pattern for texture classification. Pattern Recognition, 132, 108901. https://doi.org/10.1016/j.patcog.2022.108901
Scale-selective and noise-robust extended local binary pattern for texture classification
|Author:||Luo, Qiwu1; Su, Jiaojiao1; Yang, Chunhua1;|
1Central South University, Changsha 410083, China
2University of Oulu, Oulu 90014, Finland
3National University of Defense Technology, Changsha 410073, China
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023032433122
|Publish Date:|| 2024-07-27
As one of the most successful local feature descriptors, the local binary pattern (LBP) estimates the texture distribution rule of an image based on the signs of differences between neighboring pixels to obtain intensity- and rotation- invariance. In this paper, we propose a novel image descriptor to address scale transformation and noise interference simultaneously. We name it scale-selective and noise-robust extended LBP (SNELBP). First, each image in training sets is transformed into different scale spaces by a Gaussian filter. Second, noise-robust pattern histograms are obtained from each scale space by using our previously proposed median robust extended LBP (MRELBP). Then, scale-invariant histograms are determined by selecting the maximum among all scale levels for a certain image. Finally, the most informative patterns are selected from the dictionary pretrained by the two-stage compact dominant feature selection method (CDFS), maintaining the descriptor more lightweight with sufficiently low time cost. Extensive experiments on five public databases (Outex_TC_00011, TC_00012, KTH-TIPS, UMD and NEU) and one fresh texture database (JoJo) under two kinds of interferences (Gaussian and salt pepper) indicate that our SNELBP yields more competitive results than thirty classical LPB variants as well as eight typical deep learning methods.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the National Natural Science Foundation of China under Grant 61973323, and Grant 62111530071, and by the Hunan Provincial Natural Science Foundation of China under Grant 2021JJ20078, by the Science and Technology Innovation Program of Hunan Province under Grant 2021RC3019 and Grant 2021RC1001, and in part by the Innovation and Development Project of Ministry of Industry and Information Technology of the People's Republic of China under Grant TC19084DY.
© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license