Effective classification of birds’ species based on transfer learning

Alswaitti, M, Zihao, L, Alomoush, W, Alrosan, A and Alissa, K (2022) Effective classification of birds’ species based on transfer learning. International Journal of Electrical and Computer Engineering (IJECE), 12 (4). pp. 4172-4184. ISSN 2088-8708

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In recent years, with the deterioration of the earth’s ecological environment, the survival of birds has been more threatened. To protect birds and the diversity of species on earth, it is urgent to build an automatic bird image recognition system. Therefore, this paper assesses the performance of traditional machine learning and deep learning models on image recognition. Also, the help-ability of transfer learning in the field of image recognition is tested to evaluate the best model for bird recognition systems. Three groups of classifiers for bird recognition were constructed, namely, classifiers based on the traditional machine learning algorithms, convolutional neural networks, and transfer learning-based convolutional neural networks. After experiments, these three classifiers showed significant differences in the classification effect on the Kaggle-180-birds dataset. The experimental results finally prove that deep learning is more effective than traditional machine learning algorithms in image recognition as the number of bird species increases. Besides, the obtained results show that when the sample data is small, transfer learning can help the deep neural network classifier to improve classification accuracy.

Affiliation: Skyline University College
SUC Author(s): Alomoush, W ORCID: https://orcid.org/0000-0002-2937-4327 and Alrosan, A ORCID: https://orcid.org/0000-0001-9400-4077
All Author(s): Alswaitti, M, Zihao, L, Alomoush, W, Alrosan, A and Alissa, K
Item Type: Article
Uncontrolled Keywords: Birds’ classification; Deep learning; Machine learning; Transfer learning;
Subjects: B Information Technology > BL Machine Learning
Divisions: Skyline University College > School of Business
Depositing User: Mr Veeramani Rasu
Date Deposited: 23 May 2022 17:02
Last Modified: 18 Jan 2024 07:29
URI: https://research.skylineuniversity.ac.ae/id/eprint/214
Publisher URL: https://DOI: 10.11591/ijece.v12i4.pp4172-4184
Publisher OA policy: https://v2.sherpa.ac.uk/id/publication/23074
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