Automated Image Captioning: Harnessing Machine Learning for Image Description Generation
Keywords:
Computer Vision, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Xception, Flicker 8K, LSTM, PreprocessingAbstract
Advancements in computer vision have led to its widespread application across various domains.
This project focuses on a specific aspect of computer vision: image captioning. While generating descriptive
language for images remains a challenging task, recent research has made significant progress, particularly in
the realm of still images. Although earlier efforts primarily concentrated on video content, there has been a shift
towards enhancing image descriptions using natural language understandable to humans. Our project aims to
leverage convolutional neural networks (CNNs) and explore various hyperparameters using extensive datasets
such as Flickr8k and ResNet. By combining the outputs of these image classifiers with recurrent neural
networks (RNNs), we seek to generate accurate captions for images. This paper provides a comprehensive
overview of the architecture and methodology employed in our image captioning model.
