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Deep Learning-based Video Summarization

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@article{IJASEIT12888,
   author = {Myoungchan Seo and YoungJin Suh and Kyuman Jeong},
   title = {Deep Learning-based Video Summarization},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {6},
   year = {2021},
   pages = {2488--2494},
   keywords = {Deep learning; video summarization; scene extraction; convolutional neural network; optical flow.},
   abstract = {

With the development of communication technology, many different kinds of media transmission have become popular. Among various media, video is the most popular media these days. However, users need to spend much time watching the whole video content. Due to the characteristics of video media, many users tend to playback video content quickly or even stop watching in the middle. Some websites provide summary images by capturing only important frames of video content, which is called a video summary. Users can shorten the viewing time by only watching the summary results. In particular, it is highly useful because content such as news articles or speeches can be delivered and utilized quickly. Since video summarization is a labor-intensive task, there is an increasing demand for research on automation techniques. In this paper, an automated process to solve the temporary problem of existing video summary techniques is proposed. The proposed method improves the existing video summarization methods that have been performed manually through human labor by developing artificial intelligence technology that can effectively perform content delivery using video summary automation. In the preprocessing process, the information transfer unit is partitioned using optical flow. In the following process, CNN (Convolutional Neural Network) is used as an in-depth learning method for feature extraction. The results show the efficiency of the proposed algorithm, and some future work will be given in the end.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12888},    doi = {10.18517/ijaseit.11.6.12888} }

EndNote

%A Seo, Myoungchan
%A Suh, YoungJin
%A Jeong, Kyuman
%D 2021
%T Deep Learning-based Video Summarization
%B 2021
%9 Deep learning; video summarization; scene extraction; convolutional neural network; optical flow.
%! Deep Learning-based Video Summarization
%K Deep learning; video summarization; scene extraction; convolutional neural network; optical flow.
%X 

With the development of communication technology, many different kinds of media transmission have become popular. Among various media, video is the most popular media these days. However, users need to spend much time watching the whole video content. Due to the characteristics of video media, many users tend to playback video content quickly or even stop watching in the middle. Some websites provide summary images by capturing only important frames of video content, which is called a video summary. Users can shorten the viewing time by only watching the summary results. In particular, it is highly useful because content such as news articles or speeches can be delivered and utilized quickly. Since video summarization is a labor-intensive task, there is an increasing demand for research on automation techniques. In this paper, an automated process to solve the temporary problem of existing video summary techniques is proposed. The proposed method improves the existing video summarization methods that have been performed manually through human labor by developing artificial intelligence technology that can effectively perform content delivery using video summary automation. In the preprocessing process, the information transfer unit is partitioned using optical flow. In the following process, CNN (Convolutional Neural Network) is used as an in-depth learning method for feature extraction. The results show the efficiency of the proposed algorithm, and some future work will be given in the end.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12888 %R doi:10.18517/ijaseit.11.6.12888 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 6 %@ 2088-5334

IEEE

Myoungchan Seo,YoungJin Suh and Kyuman Jeong,"Deep Learning-based Video Summarization," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, pp. 2488-2494, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.12888.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Seo, Myoungchan
AU  - Suh, YoungJin
AU  - Jeong, Kyuman
PY  - 2021
TI  - Deep Learning-based Video Summarization
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2  - 2021
SP  - 2488
EP  - 2494
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Deep learning; video summarization; scene extraction; convolutional neural network; optical flow.
N2  - 

With the development of communication technology, many different kinds of media transmission have become popular. Among various media, video is the most popular media these days. However, users need to spend much time watching the whole video content. Due to the characteristics of video media, many users tend to playback video content quickly or even stop watching in the middle. Some websites provide summary images by capturing only important frames of video content, which is called a video summary. Users can shorten the viewing time by only watching the summary results. In particular, it is highly useful because content such as news articles or speeches can be delivered and utilized quickly. Since video summarization is a labor-intensive task, there is an increasing demand for research on automation techniques. In this paper, an automated process to solve the temporary problem of existing video summary techniques is proposed. The proposed method improves the existing video summarization methods that have been performed manually through human labor by developing artificial intelligence technology that can effectively perform content delivery using video summary automation. In the preprocessing process, the information transfer unit is partitioned using optical flow. In the following process, CNN (Convolutional Neural Network) is used as an in-depth learning method for feature extraction. The results show the efficiency of the proposed algorithm, and some future work will be given in the end.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12888 DO - 10.18517/ijaseit.11.6.12888

RefWorks

RT Journal Article
ID 12888
A1 Seo, Myoungchan
A1 Suh, YoungJin
A1 Jeong, Kyuman
T1 Deep Learning-based Video Summarization
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2488
OP 2494
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Deep learning; video summarization; scene extraction; convolutional neural network; optical flow.
AB 

With the development of communication technology, many different kinds of media transmission have become popular. Among various media, video is the most popular media these days. However, users need to spend much time watching the whole video content. Due to the characteristics of video media, many users tend to playback video content quickly or even stop watching in the middle. Some websites provide summary images by capturing only important frames of video content, which is called a video summary. Users can shorten the viewing time by only watching the summary results. In particular, it is highly useful because content such as news articles or speeches can be delivered and utilized quickly. Since video summarization is a labor-intensive task, there is an increasing demand for research on automation techniques. In this paper, an automated process to solve the temporary problem of existing video summary techniques is proposed. The proposed method improves the existing video summarization methods that have been performed manually through human labor by developing artificial intelligence technology that can effectively perform content delivery using video summary automation. In the preprocessing process, the information transfer unit is partitioned using optical flow. In the following process, CNN (Convolutional Neural Network) is used as an in-depth learning method for feature extraction. The results show the efficiency of the proposed algorithm, and some future work will be given in the end.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=12888 DO - 10.18517/ijaseit.11.6.12888