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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 4, 2024.
Abstract: Video summarization is a complex computer vision task that involves the compression of lengthy videos into shorter yet informative summaries that retain the crucial content of the original footage. This paper presents a content-based video summarization approach that utilizes superframe segmentation to identify and extract keyframes representing the most significant information in a video. Unlike other methods that rely solely on visual cues, our approach segments the video into meaningful and coherent visual content units while also preserving the original video's temporal coherence. This method helps keep the context and continuity of the video in the summary. It involves dividing the video into superframes, each of which is a cluster of adjacent frames with similar motion and visual characteristics. The superframes are then ranked based on their salient scores, which are calculated using visual and motion features. The proposed method selects the top-ranked super frames for the video summary. It has been evaluated on the SUMMe and TVSum datasets and achieved state-of-the-art results for F1-score and accuracy. Based on the experimental outcomes, it is evident that the suggested superframe segmentation method is effective for video summarization, which could be largely assistive for monitoring and controlling the student activities, particularly during their online exams.
Priyanka Ganesan, Senthil Kumar Jagatheesaperumal, Abirami R, Lekhasri K, Silvia Gaftandzhieva and Rositsa Doneva, “Superframe Segmentation for Content-based Video Summarization” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150425
@article{Ganesan2024,
title = {Superframe Segmentation for Content-based Video Summarization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150425},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150425},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Priyanka Ganesan and Senthil Kumar Jagatheesaperumal and Abirami R and Lekhasri K and Silvia Gaftandzhieva and Rositsa Doneva}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.