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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/11/1285 |
| _version_ | 1797548089129566208 |
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| author | WenLin Li DeYu Qi ChangJian Zhang Jing Guo JiaJun Yao |
| author_facet | WenLin Li DeYu Qi ChangJian Zhang Jing Guo JiaJun Yao |
| author_sort | WenLin Li |
| collection | DOAJ |
| 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<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mi>m</mi><mi>e</mi><mi>a</mi><mi>s</mi><mi>u</mi><mi>r</mi><mi>e</mi></mrow></msub></semantics></math></inline-formula> 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. |
| first_indexed | 2024-03-10T14:53:27Z |
| format | Article |
| id | doaj.art-032c070a8fc842cdbf54186c4a6b66f6 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T14:53:27Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-032c070a8fc842cdbf54186c4a6b66f62023-11-20T20:43:41ZengMDPI AGEntropy1099-43002020-11-012211128510.3390/e22111285Video Summarization Based on Mutual Information and Entropy Sliding Window MethodWenLin Li0DeYu Qi1ChangJian Zhang2Jing Guo3JiaJun Yao4School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou 510006, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou 510006, ChinaThis 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<inline-formula><math display="inline"><semantics><msub><mrow></mrow><mrow><mi>m</mi><mi>e</mi><mi>a</mi><mi>s</mi><mi>u</mi><mi>r</mi><mi>e</mi></mrow></msub></semantics></math></inline-formula> 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.https://www.mdpi.com/1099-4300/22/11/1285entropyvideo summarizationkey frame extractionvideo analysisgesture videosfeature extraction |
| spellingShingle | WenLin Li DeYu Qi ChangJian Zhang Jing Guo JiaJun Yao Video Summarization Based on Mutual Information and Entropy Sliding Window Method Entropy entropy video summarization key frame extraction video analysis gesture videos feature extraction |
| 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 | entropy video summarization key frame extraction video analysis gesture videos feature extraction |
| url | https://www.mdpi.com/1099-4300/22/11/1285 |
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