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Fast temporal video segmentation based on Krawtchouk-Tchebichef moments

With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal...

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Main Authors: Abdulhussain, Sadiq H., Al-Haddad, Syed Abdul Rahman, Saripan, M. Iqbal, Mahmood, Basheera M., Hussien, Aseel
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2020
Online Access:http://psasir.upm.edu.my/id/eprint/89000/1/FAST.pdf
http://psasir.upm.edu.my/id/eprint/89000/
https://ieeexplore.ieee.org/abstract/document/9066918
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spelling my.upm.eprints.890002021-10-04T21:49:57Z http://psasir.upm.edu.my/id/eprint/89000/ Fast temporal video segmentation based on Krawtchouk-Tchebichef moments Abdulhussain, Sadiq H. Al-Haddad, Syed Abdul Rahman Saripan, M. Iqbal Mahmood, Basheera M. Hussien, Aseel With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal video segmentation (TVS) is considered essential stage in content-based video indexing and retrieval system. TVS aims to detect boundaries between successive video shots. TVS algorithm design is still challenging because most of the recent methods are unable to achieve fast and robust detection. In this regard, this paper proposes a TVS algorithm with high precision and recall values, and low computation cost for detecting different types of video transitions. The proposed algorithm is based on orthogonal moments which are considered as features to detect transitions. To increase the speed of the TVS algorithm as well as the accuracy, fast block processing and embedded orthogonal polynomial algorithms are utilized to extract features. This utilization will lead to extract multiple local features with low computational cost. Support vector machine (SVM) classifier is used to detect transitions. Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. In addition, the performance of the proposed algorithm is compared to several state-of-the-art TVS algorithms. Experimental results demonstrated that the proposed algorithm performance improvements in terms of recall, precision, and F1-score are within the ranges (1.31 - 2.58), (1.53 - 4.28), and (1.41 - 3.03), respectively. Moreover, the proposed method shows low computation cost which is 2% of real-time. Institute of Electrical and Electronics Engineers 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/89000/1/FAST.pdf Abdulhussain, Sadiq H. and Al-Haddad, Syed Abdul Rahman and Saripan, M. Iqbal and Mahmood, Basheera M. and Hussien, Aseel (2020) Fast temporal video segmentation based on Krawtchouk-Tchebichef moments. IEEE Access, 8. 72347 - 72359. ISSN 2169-3536 https://ieeexplore.ieee.org/abstract/document/9066918 10.1109/ACCESS.2020.2987870
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal video segmentation (TVS) is considered essential stage in content-based video indexing and retrieval system. TVS aims to detect boundaries between successive video shots. TVS algorithm design is still challenging because most of the recent methods are unable to achieve fast and robust detection. In this regard, this paper proposes a TVS algorithm with high precision and recall values, and low computation cost for detecting different types of video transitions. The proposed algorithm is based on orthogonal moments which are considered as features to detect transitions. To increase the speed of the TVS algorithm as well as the accuracy, fast block processing and embedded orthogonal polynomial algorithms are utilized to extract features. This utilization will lead to extract multiple local features with low computational cost. Support vector machine (SVM) classifier is used to detect transitions. Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. In addition, the performance of the proposed algorithm is compared to several state-of-the-art TVS algorithms. Experimental results demonstrated that the proposed algorithm performance improvements in terms of recall, precision, and F1-score are within the ranges (1.31 - 2.58), (1.53 - 4.28), and (1.41 - 3.03), respectively. Moreover, the proposed method shows low computation cost which is 2% of real-time.
format Article
author Abdulhussain, Sadiq H.
Al-Haddad, Syed Abdul Rahman
Saripan, M. Iqbal
Mahmood, Basheera M.
Hussien, Aseel
spellingShingle Abdulhussain, Sadiq H.
Al-Haddad, Syed Abdul Rahman
Saripan, M. Iqbal
Mahmood, Basheera M.
Hussien, Aseel
Fast temporal video segmentation based on Krawtchouk-Tchebichef moments
author_facet Abdulhussain, Sadiq H.
Al-Haddad, Syed Abdul Rahman
Saripan, M. Iqbal
Mahmood, Basheera M.
Hussien, Aseel
author_sort Abdulhussain, Sadiq H.
title Fast temporal video segmentation based on Krawtchouk-Tchebichef moments
title_short Fast temporal video segmentation based on Krawtchouk-Tchebichef moments
title_full Fast temporal video segmentation based on Krawtchouk-Tchebichef moments
title_fullStr Fast temporal video segmentation based on Krawtchouk-Tchebichef moments
title_full_unstemmed Fast temporal video segmentation based on Krawtchouk-Tchebichef moments
title_sort fast temporal video segmentation based on krawtchouk-tchebichef moments
publisher Institute of Electrical and Electronics Engineers
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/89000/1/FAST.pdf
http://psasir.upm.edu.my/id/eprint/89000/
https://ieeexplore.ieee.org/abstract/document/9066918
_version_ 1713201323185274880
score 13.211869

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