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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

Date: September 15,2022 Hits: 377


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.


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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 Computation6(3), 343-346.

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