Shot boundary detection based on orthogonal polynomial

Shot boundary detection (SBD) is a substantial step in video content analysis, indexing, retrieval, and summarization. SBD is the process of automatically partitioning video into its basic units, known as shots, through detecting transitions between shots. The design of SBD algorithms developed from...

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Published in:	Multimedia tools and applications Vol. 78; no. 14; pp. 20361 - 20382
Main Authors:	Abdulhussain, Sadiq H., Ramli, Abd Rahman, Mahmmod, Basheera M., Saripan, M. Iqbal, Al-Haddad, S. A. R., Jassim, Wissam A.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.07.2019 Springer Nature B.V
Subjects:	Algorithms Computer Communication Networks Computer Science Computer vision Content analysis Data Structures and Information Theory Multimedia Information Systems Polynomials Redundancy Signal processing Special Purpose and Application-Based Systems Support vector machines Video data Abrupt transition Hard transition Temporal video segmentation Shot boundary detection Orthogonal polynomial Orthogonal moments
ISSN:	1380-7501, 1573-7721
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Abstract	Shot boundary detection (SBD) is a substantial step in video content analysis, indexing, retrieval, and summarization. SBD is the process of automatically partitioning video into its basic units, known as shots, through detecting transitions between shots. The design of SBD algorithms developed from simple feature comparison to rigorous probabilistic and using of complex models. Nevertheless, accelerate the detection of transitions with higher accuracy need to be improved. Extensive research has employed orthogonal polynomial (OP) and their moments in computer vision and signal processing owing to their powerful performance in analyzing signals. A new SBD algorithm based on OP has been proposed in this paper. The Features are derived from orthogonal transform domain (moments) to detect the hard transitions in video sequences. Moments are used because of their ability to represent signal (video frame) without information redundancy. These features are the moments of smoothed and gradients of video frames. The moments are computed using a developed OP which is squared Krawtchouk-Tchebichef polynomial. These moments (smoothed and gradients) are fused to form a feature vector. Finally, the support vector machine is utilized to detect hard transitions. In addition, a comparison between the proposed algorithm and other state-of-the-art algorithms is performed to reinforce the capability of the proposed work. The proposed algorithm is examined using three well-known datasets which are TRECVID2005, TRECVID2006, and TRECVID2007. The outcomes of the comparative analysis show the superior performance of the proposed algorithm against other existing algorithms.
AbstractList	Shot boundary detection (SBD) is a substantial step in video content analysis, indexing, retrieval, and summarization. SBD is the process of automatically partitioning video into its basic units, known as shots, through detecting transitions between shots. The design of SBD algorithms developed from simple feature comparison to rigorous probabilistic and using of complex models. Nevertheless, accelerate the detection of transitions with higher accuracy need to be improved. Extensive research has employed orthogonal polynomial (OP) and their moments in computer vision and signal processing owing to their powerful performance in analyzing signals. A new SBD algorithm based on OP has been proposed in this paper. The Features are derived from orthogonal transform domain (moments) to detect the hard transitions in video sequences. Moments are used because of their ability to represent signal (video frame) without information redundancy. These features are the moments of smoothed and gradients of video frames. The moments are computed using a developed OP which is squared Krawtchouk-Tchebichef polynomial. These moments (smoothed and gradients) are fused to form a feature vector. Finally, the support vector machine is utilized to detect hard transitions. In addition, a comparison between the proposed algorithm and other state-of-the-art algorithms is performed to reinforce the capability of the proposed work. The proposed algorithm is examined using three well-known datasets which are TRECVID2005, TRECVID2006, and TRECVID2007. The outcomes of the comparative analysis show the superior performance of the proposed algorithm against other existing algorithms.
Author	Al-Haddad, S. A. R. Mahmmod, Basheera M. Saripan, M. Iqbal Abdulhussain, Sadiq H. Ramli, Abd Rahman Jassim, Wissam A.
Author_xml	– sequence: 1 givenname: Sadiq H. orcidid: 0000-0002-6439-0082 surname: Abdulhussain fullname: Abdulhussain, Sadiq H. email: sadiqh76@yahoo.com, sadiqhabeeb@coeng.uobaghdad.edu.iq organization: Department of Computer Engineering, University of Baghdad – sequence: 2 givenname: Abd Rahman surname: Ramli fullname: Ramli, Abd Rahman organization: Department of Computer and Communication System Engineering, Universiti Putra Malaysia – sequence: 3 givenname: Basheera M. surname: Mahmmod fullname: Mahmmod, Basheera M. organization: Department of Computer Engineering, University of Baghdad – sequence: 4 givenname: M. Iqbal surname: Saripan fullname: Saripan, M. Iqbal organization: Department of Computer and Communication System Engineering, Universiti Putra Malaysia – sequence: 5 givenname: S. A. R. surname: Al-Haddad fullname: Al-Haddad, S. A. R. organization: Department of Computer and Communication System Engineering, Universiti Putra Malaysia – sequence: 6 givenname: Wissam A. surname: Jassim fullname: Jassim, Wissam A. organization: ADAPT Center, School of Engineering, Trinity College Dublin, University of Dublin
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Keywords	Abrupt transition Hard transition Temporal video segmentation Shot boundary detection Orthogonal polynomial Orthogonal moments
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Snippet	Shot boundary detection (SBD) is a substantial step in video content analysis, indexing, retrieval, and summarization. SBD is the process of automatically...
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SubjectTerms	Algorithms Computer Communication Networks Computer Science Computer vision Content analysis Data Structures and Information Theory Multimedia Information Systems Polynomials Redundancy Signal processing Special Purpose and Application-Based Systems Support vector machines Video data
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Title	Shot boundary detection based on orthogonal polynomial
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