Recuperating Image Captioning with Genetic Algorithm and Red Deer Optimization: A Comparative Study

Image captioning, a research domain of Artificial Intelligence, combines Computer Vision (CV) and Natural Language Processing (NLP) to spawn descriptive captions for images. This manuscript focuses on enhancing image captioning performance using the Flickr8k dataset through the application of two optimization techniques: Red Deer Optimization (RDO) and Genetic Algorithm (GA). These optimization algorithms are employed to progress the excellence and efficiency of image description. The study explores their effectiveness in generating accurate and contextually relevant captions. By leveraging the advantages of Red Deer Optimization and Genetic Algorithm, we aim to achieve enhanced results in image captioning, leading to improved accessibility for visually impaired individuals, enhanced search engine performance, and more efficient image indexing. The findings of this research shed light on the potential of these optimization techniques in advancing the field of image captioning and contributing to its real-world applications.