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Journal of Applied Mathematics and Computation

ISSN Print: 2576-0645 Downloads: 138967 Total View: 1745951
Frequency: quarterly ISSN Online: 2576-0653 CODEN: JAMCEZ
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Article http://dx.doi.org/10.26855/jamc.2022.09.006

Comparisons of Image Classification Using LBP with CNN and ANN

Urmila Samariya1,2, Rakesh K. Sonker3,4,*

1Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi 110006, India.

2School of Information Technology & Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014, India.

3Department of Physics, Acharya Narendra Dev College, University of Delhi, Delhi 110019, India.

4Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India.

*Corresponding author: Rakesh K. Sonker

Published: September 15,2022

Abstract

In image classification techniques CNN (convolutional neural networks) has a strong feature extraction capability, which has been used to extract the feature from different class image datasets. Various techniques have been applied to compare the images and to gain the accuracy level. Convolutional neural network and many other algorithms can be for feature extraction like Decision tree, K-Nearest neighbor, and nearest clustering algorithms. In this paper, we performed image classification using CNN and an artificial neural network (ANN) with local binary pattern (LBP) feature extraction technique. One-dimensional (1-D) CNN is implemented to process the feature extraction. The network is trained using the Kaggle dataset of birds and airplanes. The trained classifier model can classify the images into either birds or airplanes. The experimental outcome shows that the proposed technique can provide 77.50 and 62.42 classification accuracy, and the proposed method can also achieve better performance using different datasets.

References

[1] Siswantoro, J., Prabuwono, A. S., Abdullah, A., & Idrus, B. (2016). A linear model based on Kalman filter for improving neural network classification performance. Expert Systems with Applications, 49, 112-122.

[2] KAVITHA, D., and V. RADHA. "TEXNET: A DEEP CONVOLUTIONAL NEURAL NETWORK MODEL TO RECOGNIZE TEXT IN NATURAL SCENE IMAGES." Journal of Engineering Science and Technology, 16.2 (2021): 1782-1799.

[3] Garrido, C., De Oña, R. and De Oña, J. (2014). Neural networks for analyzing service quality in public transportation. Expert Systems with Applications, 41(15), pp. 6830-6838.

[4] Laughlin, S. B., & Sejnowski, T. J. (2003). Communication in neuronal networks. Science, 301(5641), 1870-1874.

[5] Rawat, Waseem, and Zenghui Wang. (2017). "Deep convolutional neural networks for image classification: A comprehensive review." Neural computation 29.9 (2017): 2352-2449.

[6] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016, July). Breast cancer histopathological image classification using convolutional neural networks. In 2016 international joint conference on neural networks (IJCNN) (pp. 2560-2567). IEEE.

[7] Cavalin, P., & Oliveira, L. S. (2017, October). A review of texture classification methods and databases. In 2017 30th SIBGRAPI Conference on graphics, patterns and images tutorials (SIBGRAPI-T) (pp. 1-8). IEEE.

[8] Zhang, Hongshuai, Zhiyi Qu, Liping Yuan, and Gang Li. (2017). "A face recognition method based on LBP feature for CNN." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 544-547. IEEE, 2017.

[9] Wei, X., Yu, X., Liu, B. and Zhi, L. (2019). Convolutional neural networks and local binary patterns for hyperspectral image classification. European Journal of Remote Sensing, 52(1), pp. 448-462.

[10] Omar, Mohamed, Fouad Khelifi, and Muhammad Atif Tahir. (2016). "Detection and classification of retinal fundus images exudates using region based multiscale LBP texture approach." In 2016 international conference on control, decision and information technologies (CoDIT), pp. 227-232. IEEE, 2016.

[11] Ojala, T., Pietikäinen, M. and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), pp. 51-59.

[12] Bourlard, H. and Morgan, N. (1993). Continuous speech recognition by connectionist statistical methods. IEEE Transactions on Neural Networks, 4(6), pp.893-909.

[13] Knerr, Stefan, Léon Personnaz, and Gérard Dreyfus. (1992). "Handwritten digit recognition by neural networks with single-layer training." IEEE Transactions on neural networks 3.6 (1992): 962-968.

[14] Hu, Wei, et al. (2015). "Deep convolutional neural networks for hyperspectral image classification." Journal of Sensors 2015 (2015).

How to cite this paper

Comparisons of Image Classification Using LBP with CNN and ANN

How to cite this paper:  Urmila Samariya, Rakesh K. Sonker. (2022) Comparisons of Image Classification Using LBP with CNN and ANN. Journal of Applied Mathematics and Computation, 6(3), 343-346.

DOI: https://dx.doi.org/10.26855/jamc.2022.09.006

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