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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

V. R. Kavitha1, K. Nimala2, A. Beno3, K. C. Ramya4, Seifedine Kadry5, Byeong-Gwon Kang6, Yunyoung Nam7,*

1 Department of Computer Science and Engineering, Prathyusha Engineering College, Thiruvallur, 602025, India
2 Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India
3 Department of Electronics and Communication Engineering, Dr. Sivanthi Aditanar College of Engineering, Tiruchendur, 628215, India
4 Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India
5 Deparmtent of Applied Data Science, Noroff University College, Kristiansand, Norway
6 Department of Information and Communication Engineering, Soonchunhyang University, Asan, Korea
7 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea

* Corresponding Author: Yunyoung Nam. Email:

Computer Systems Science and Engineering 2023, 44(1), 579-593.


Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI). It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions. It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc. The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics. In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHO-DLIC) technique. The OHHO-DLIC technique involves the design of distinct levels of pre-processing. Moreover, the feature extraction of the images is carried out by the use of EfficientNet model. Furthermore, the image captioning is performed by bidirectional long short term memory (BiLSTM) model, comprising encoder as well as decoder. At last, the oppositional Harris Hawks optimization (OHHO) based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models. The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.


Cite This Article

V. R. Kavitha, K. Nimala, A. Beno, K. C. Ramya, S. Kadry et al., "Oppositional harris hawks optimization with deep learning-based image captioning," Computer Systems Science and Engineering, vol. 44, no.1, pp. 579–593, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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