A survey on machine learning techniques for semantic image and video annotations
- DOI
- 10.2991/978-94-6463-496-9_14How to use a DOI?
- Keywords
- Machine Learning Techniques; Image Annotation; Video Annotation; Generative Models; Discriminative Models
- Abstract
Due to the rapid growth of digital images from sources such as social media and medical imaging, the need for efficient and accurate annotation methods is increasing. While traditional manual annotation is time-consuming and not scalable, machine learning has revolutionized this process by automating the extraction of semantic keywords. This study reviews generative models, discriminative models and trends and Advances in Image and Video Sequences Annotations, highlighting strengths, weaknesses, applications, and recent developments. This study aims to enhance the development and understanding of new annotation methods and improve existing pipelines in visual content analysis.
- Copyright
- © 2024 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.
Cite this article
TY - CONF AU - Laib Lakhdar AU - Mohand Saïd Allili PY - 2024 DA - 2024/08/31 TI - A survey on machine learning techniques for semantic image and video annotations BT - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024) PB - Atlantis Press SP - 171 EP - 184 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-496-9_14 DO - 10.2991/978-94-6463-496-9_14 ID - Lakhdar2024 ER -