Volume 18, Issue 4 (3-2022)
JSDP 2022, 18(4): 165-180 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Jalalian Shahri M R, Hadizadeh H, Khademi Darah M, Ebrahimi Moghadam A. A Novel Noise-Robust Texture Classification Method Using Joint Multiscale LBP. JSDP 2022; 18 (4) : 10
URL: http://jsdp.rcisp.ac.ir/article-1-924-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-924-en.html
Ferdowsi University of Mashhad
Abstract: (1614 Views)
In this paper we describe a novel noise-robust texture classification method using joint multiscale local binary pattern. The first step in texture classification is to describe the texture by extracting different features. So far, several methods have been developed for this topic, one of the most popular ones is Local Binary Pattern (LBP) method and its variants such as Completed Local Binary Pattern, Extended Local Binary Pattern, Local Temporary Pattern, Local Contrast Pattern, etc. In order to extract the features of a texture in different scales, the LBP method can be implemented in a multi-scale framework. For this purpose, the extracted feature vectors at different scales are usually concatenated together to produce the final feature vector with a longer length. But such a scheme has two main shortcomings. First, the LBP method is very sensitive to noise, hence by adding noise to a texture image, its feature vectors may change significantly. Second, by increasing the number of the scales, the length of the final feature vector is increased accordingly. This action increases the classification process time, and it may reduce the classification accuracy. To mitigate these shortcomings, this paper presents a method based on multiscale LBP, which has a better resistance against white Gaussian noise, while the length of its final feature vector is equal to the length of the final feature vector produced by the original LBP method. To implement the proposed method, we used 17 circular binary masks that contain 8 directed first-order masks, 8 directed second-order masks and 1 undirected mask. These masks have positive and negative weightes and each group of these masks have different radius which after convolution with input image extract features in different scales. Experiments were performed on four test groups of Outex database. Experimental results show that the proposed method is superior to the existing state-of-the-art methods. The complexity of proposed method is also analyzed. The results show that in this method, despite obtaining excellent classification accuracy, the complexity of the method has not changed much and even its complexity is less than some of the existing state-of-the-art methods.
Article number: 10
Keywords: feature extraction, Local Binary Pattern, texture, texture classification, white gaussian noise
Type of Study: Research |
Subject:
Paper
Received: 2018/11/6 | Accepted: 2022/01/8 | Published: 2022/03/21 | ePublished: 2022/03/21
Received: 2018/11/6 | Accepted: 2022/01/8 | Published: 2022/03/21 | ePublished: 2022/03/21
References
1. [1] M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen, Computer vision using local binary patterns vol. 40: Springer Science & Business Media, 2011. [DOI:10.1007/978-0-85729-748-8]
2. [2] Y. Dong, J. Feng, L. Liang, L. Zheng, and Q. Wu, "Multiscale sampling based texture image classification," IEEE Signal Processing Letters, vol. 24, pp. 614-618, 2017. [DOI:10.1109/LSP.2017.2670026]
3. [3] V.-L. Nguyen, N.-S. Vu, and P.-H. Gosselin, "A scattering transform combination with local binary pattern for texture classification," in International Workshop on Content-based Multimedia Indexing, 2016. [DOI:10.1109/CBMI.2016.7500238]
4. [4] F. Bianconi and A. Fernández, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, vol. 40, pp. 3325-3335, 2007. [DOI:10.1016/j.patcog.2007.04.023]
5. [5] J. Oh, S.-I. Choi, C. Kim, J. Cho, and C.-H. Choi, "Selective generation of Gabor features for fast face recognition on mobile devices," Pattern Recognition Letters, vol. 34, pp. 1540-1547, 2013. [DOI:10.1016/j.patrec.2013.06.009]
6. [6] P. Cavalin, L. Oliveira, A. Koerich, and A. Britto, "Wood defect detection using grayscale images and an optimized feature set," in IEEE Industrial Electronics, IECON 2006-32nd Annual Conference on, 2006, pp. 3408-3412. [DOI:10.1109/IECON.2006.347618]
7. [7] P. R. Cavalin, M. N. Kapp, J. Martins, and L. E. Oliveira, "A multiple feature vector framework for forest species recognition," in Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013, pp. 16-20. [DOI:10.1145/2480362.2480368]
8. [8] R. M. Haralick and K. Shanmugam, "Textural features for image classification," IEEE Transactions on systems, man, and cybernetics, pp. 610-621, 1973. [DOI:10.1109/TSMC.1973.4309314]
9. [9] Z. Guo, L. Zhang, and D. Zhang, "A completed modeling of local binary pattern operator for texture classification," IEEE Transactions on Image Processing, vol. 19, pp. 1657-1663, 2010. [DOI:10.1109/TIP.2010.2044957] [PMID]
10. [10] L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang, and M. Pietikäinen, "Median robust extended local binary pattern for texture classification," IEEE Transactions on Image Processing, vol. 25, pp. 1368-1381, 2016. [DOI:10.1109/TIP.2016.2522378] [PMID]
11. [11] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, "Extended local binary patterns for texture classification," Image and Vision Computing, vol. 30, pp. 86-99, 2012. [DOI:10.1016/j.imavis.2012.01.001]
12. [12] T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, pp. 51-59, 1996. [DOI:10.1016/0031-3203(95)00067-4]
13. [13] X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE transactions on image processing, vol. 19, pp. 1635-1650, 2010. [DOI:10.1109/TIP.2010.2042645] [PMID]
14. [14] T. Song, H. Li, F. Meng, Q. Wu, B. Luo, B. Zeng, et al., "Noise-robust texture description using local contrast patterns via global measures," IEEE Signal Processing Letters, vol. 21, pp. 93-96, 2014. [DOI:10.1109/LSP.2013.2293335]
15. [15] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on pattern analysis and machine intelligence, vol. 24, pp. 971-987, 2002. [DOI:10.1109/TPAMI.2002.1017623]
16. [16] T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen, and S. Huovinen, "Outex-new framework for empirical evaluation of texture analysis algorithms," in Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2002, pp. 701-706.
17. [17] J. He, H. Ji, and X. Yang, "Rotation invariant texture descriptor using local shearlet-based energy histograms," IEEE Signal Processing Letters, vol. 20, pp. 905-908, 2013. [DOI:10.1109/LSP.2013.2267730]
18. [18] I. El khadiri, A. Chahi, Y. El-Merabet, Y. Ruichek and R. Touahni, "Image classification with Local Directional Decoded Ternary Pattern," 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 2019, pp. 812-817, [DOI:10.1109/CoDIT.2019.8820373]
19. [19] S. R. Barburiceanu, S. Meza, C. Germain and R. Terebes, "An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness," 2019 27th European Signal Processing Conference (EUSIPCO), 2019, pp. 1-5 [DOI:10.23919/EUSIPCO.2019.8902765]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
