Version 1
: Received: 17 April 2024 / Approved: 18 April 2024 / Online: 18 April 2024 (13:59:05 CEST)
How to cite:
Peronikolis, M.; Panagiotakis, C. Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets. Preprints2024, 2024041241. https://doi.org/10.20944/preprints202404.1241.v1
Peronikolis, M.; Panagiotakis, C. Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets. Preprints 2024, 2024041241. https://doi.org/10.20944/preprints202404.1241.v1
Peronikolis, M.; Panagiotakis, C. Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets. Preprints2024, 2024041241. https://doi.org/10.20944/preprints202404.1241.v1
APA Style
Peronikolis, M., & Panagiotakis, C. (2024). Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets. Preprints. https://doi.org/10.20944/preprints202404.1241.v1
Chicago/Turabian Style
Peronikolis, M. and Costas Panagiotakis. 2024 "Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets" Preprints. https://doi.org/10.20944/preprints202404.1241.v1
Abstract
In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.
Keywords
Video Summarization; recommender systems; video segmentation; personalized video summary
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
