Advancement in Image Caption Generation

DOI

10.2991/978-94-6463-738-0_47How to use a DOI?

Keywords

Image Captioning; Flickr8k; LSTM; CNN

Abstract

Image captioning represents a convergence of computer vision along with natural language processing, with the objective of creating an effective image caption generator through deep learning methodologies. This process entails annotating images with English keywords by employing datasets during the training phase of the model, leveraging both of them natural language processing integrating with computer vision to formulate captions. The dataset comprises pairs of images and their corresponding captions, where a CNN encoder is utilized to draw out semantic information arising out of the visual components, while the LSTM decoder is trained to generate captions sequentially. The findings indicate the efficacy of the deep learning approach having an accuracy of about 90%, highlighting its potential applications in diverse areas such as assistive technology and multimedia retrieval. Future research concentrate on improving model performance and enhancing contextual understanding to yield more human-like descriptions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kaamya Sarda
AU  - Anshu Mehta
AU  - Aleem Ali
PY  - 2025
DA  - 2025/06/22
TI  - Advancement in Image Caption Generation
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 586
EP  - 600
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_47
DO  - 10.2991/978-94-6463-738-0_47
ID  - Sarda2025
ER  -