Open Access

ARTICLE

Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System for Visually Impaired People

Anwer Mustafa Hilal^1,*, Fadwa Alrowais², Fahd N. Al-Wesabi³, Radwa Marzouk^4,5

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Mahayil, Saudi Arabia
4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Mathematics, Faculty of Science, Cairo University, Giza, 12613, Egypt

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 46(2), 1929-1945. https://doi.org/10.32604/csse.2023.035529

Received 24 August 2022; Accepted 14 December 2022; Issue published 09 February 2023

Abstract

The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing (NLP) and computer vision (CV). It can be driven by applications like image retrieval or indexing, virtual assistants, image understanding, and support of visually impaired people (VIP). Though the VIP uses other senses, touch and hearing, for recognizing objects and events, the quality of life of those persons is lower than the standard level. Automatic Image captioning generates captions that will be read loudly to the VIP, thereby realizing matters happening around them. This article introduces a Red Deer Optimization with Artificial Intelligence Enabled Image Captioning System (RDOAI-ICS) for Visually Impaired People. The presented RDOAI-ICS technique aids in generating image captions for VIPs. The presented RDOAI-ICS technique utilizes a neural architectural search network (NASNet) model to produce image representations. Besides, the RDOAI-ICS technique uses the radial basis function neural network (RBFNN) method to generate a textual description. To enhance the performance of the RDOAI-ICS method, the parameter optimization process takes place using the RDO algorithm for NasNet and the butterfly optimization algorithm (BOA) for the RBFNN model, showing the novelty of the work. The experimental evaluation of the RDOAI-ICS method can be tested using a benchmark dataset. The outcomes show the enhancements of the RDOAI-ICS method over other recent Image captioning approaches.

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Keywords

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