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.

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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  -