Image Captioning of an Environment Using Machine Learning Algorithms (A Case Study of Gwarzo Road, Kano Nigeria)

Muhammad Aliyu and Amir Abdullahi Bature
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Muhammad Aliyu: Research Scholar, Bayero University, Kano (Nigeria)
Amir Abdullahi Bature: Associate Proffessor, Bayero University, Kano (Nigeria)

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 10, 677-689

Abstract: This paper investigates the application of machine learning algorithms for automatic image captioning, focusing on a case study of Gwarzo Road in Kano, Nigeria. The research aims to design a robust VGG16/LSTM-based model that generates accurate and contextually relevant descriptions for images captured along the Kabuga to Bayero University Kano new site route. The methodology involves collecting images at three distinct times of the day (morning, afternoon, and evening) over 60 days, resizing and labelling them with relevant captions to build a comprehensive dataset. The VGG16 model, known for its efficiency in image processing, was employed for feature extraction, while the LSTM network was used to generate captions by interpreting the contextual and semantic details of the images. This study addresses key challenges in image captioning, such as localized object detection and generating meaningful textual descriptions, improving on existing datasets and models that often lack contextual relevance in specific environments. The expected outcomes of this research include the development of a precise caption generation model with high accuracy and efficiency. The resulting model achieved a BLEU score of 0.051, representing baseline performance in caption generation with partial alignment to human-generated references. Additionally, the model’s highest accuracy based on the loss function reached 55%, while the lowest accuracy was 50%, with an average accuracy of 53%. The creation of a localized image database further enhances the significance of this research for future applications and studies in image captioning.

Date: 2024
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