Image classification using local binary patterns

Pronin, S.V.

Full text «Opticheskii Zhurnal»

Full text on elibrary.ru

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Пронин С.В. Классификация изображений при помощи локальных бинарных паттернов // Оптический журнал. 2020. Т. 87. № 12. С. 50 –56. http://doi.org/10.17586/1023-5086-2020-87-12-50-56

For citation (Journal of Optical Technology):

S. V. Pronin, "Image classification using local binary patterns,"Journal of Optical Technology. 87(12), 738-741 (2020). https://doi.org/10.1364/JOT.87.000738

Abstract:

An image-classification algorithm based on an alphabet of local binary patterns is described. The efficiency of the proposed algorithm is demonstrated using a test image set of handwritten digits (MNIST). A comparison of the algorithm’s properties with similar results to those of convolutional artificial neural networks is presented.

Keywords:

local binary patterns, image classification, MNIST database

OCIS codes: 100.3008, 100.5010

References:

1. B. Julesz, “Textons, the elements of texture perception, and their interactions,” Nature 290, 91–97 (1981).
2. D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (Radio i Svyaz’, Moscow, 1987).
3. F. Bergeaud and S. Mallat, “Matching pursuit of images,” in Proceedings of the IEEE International Conference on Image
Processing (1995), pp. 53–56.
4. C. Guo, S.-C. Zhu, and Y. N. Wu, “Towards a mathematical theory of primal sketch and sketchability,” in 9th IEEE International Conference on Computer Vision (2003), pp. 1228–1235.
5. T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution grayscale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002).
6. S. Brahnam, L. C. Jain, L. Nanni, and A. Lumini, Local Binary Patterns: New Variants and Applications (Springer, Berlin, 2014).
7. H. Jin, Q. Liu, H. Lu, and X. Tong, “Face detection using improved LBP under Bayesian framework,” in Proceedings of the 3rd
International Conference on Image and Graphics (2004), pp. 306–309.
8. I. Nurul, A. B. W. Tjokorda, and N. R. Kurniawan, “Handwriting digit recognition using local binary pattern variance and K-nearest neighbor classification,” in 4th International Conference on Information and Communication Technology (2016).
9. D. Kruchinin, E. Dolotov, K. Kornyakov, V. Kustikova, and P. Druzhkov, “Comparison of deep learning libraries on the problem
of handwritten digit classification,” in 4th Conference on Analysis of Images, Social Networks, and Texts (2015), pp. 339–411.
10. U. G. Maestre, Effective Techniques of the Use of Data Augmentation in Classification Tasks (Universitat d’Alacant, Alicante, 2018).
11. S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer, London, 2009).