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Video Summarization Based on Mutual Information and Entropy Sliding Window Method
This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711815/ https://www.ncbi.nlm.nih.gov/pubmed/33287053 http://dx.doi.org/10.3390/e22111285 |
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author | Li, WenLin Qi, DeYu Zhang, ChangJian Guo, Jing Yao, JiaJun |
author_facet | Li, WenLin Qi, DeYu Zhang, ChangJian Guo, Jing Yao, JiaJun |
author_sort | Li, WenLin |
collection | PubMed |
description | This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and F [Formula: see text] are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video. |
format | Online Article Text |
id | pubmed-7711815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77118152021-02-24 Video Summarization Based on Mutual Information and Entropy Sliding Window Method Li, WenLin Qi, DeYu Zhang, ChangJian Guo, Jing Yao, JiaJun Entropy (Basel) Article This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and F [Formula: see text] are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video. MDPI 2020-11-12 /pmc/articles/PMC7711815/ /pubmed/33287053 http://dx.doi.org/10.3390/e22111285 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, WenLin Qi, DeYu Zhang, ChangJian Guo, Jing Yao, JiaJun Video Summarization Based on Mutual Information and Entropy Sliding Window Method |
title | Video Summarization Based on Mutual Information and Entropy Sliding Window Method |
title_full | Video Summarization Based on Mutual Information and Entropy Sliding Window Method |
title_fullStr | Video Summarization Based on Mutual Information and Entropy Sliding Window Method |
title_full_unstemmed | Video Summarization Based on Mutual Information and Entropy Sliding Window Method |
title_short | Video Summarization Based on Mutual Information and Entropy Sliding Window Method |
title_sort | video summarization based on mutual information and entropy sliding window method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711815/ https://www.ncbi.nlm.nih.gov/pubmed/33287053 http://dx.doi.org/10.3390/e22111285 |
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