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Kosin Chamnongthai. Video shot boundary detection using principal component analysis (PCA) and deep learning. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2021.
Video shot boundary detection using principal component analysis (PCA) and deep learning
Name: Dipanita Chakraborty
Organization :
King Mongkuts University of Technology Thonburi. Department of Electronic and Telecommunication Engineering
Email :
dipanita.chakraborty@mail.kmutt.ac.th
Name: Werapon Chiracharit
Organization :
King Mongkuts University of Technology Thonburi. Department of Electronic and Telecommunication Engineering
Email :
werapon.chi@kmutt.ac.th
Name: Kosin Chamnongthai
Organization :
King Mongkuts University of Technology Thonburi. Department of Electronic and Telecommunication Engineering
Email :
kosin.cha@kmutt.ac.th
keyword:
Scene segmentation
LCSH:
Machine learning
-- Applications
Abstract:
After the COVID-19 pandemic, with the tremendously increasing reliance on digital media platforms all over the world, advanced video surveillance, video content analysis, and video retrieval tasks have become essential fields, which makes traditional methods for shot boundary detection (SBD) less efficient, especially for gradual transition type shot boundaries. SBD is an important preliminary step in video processing which helps to separate video sequences into shots. Large videos (news, national ceremonies, sports, movies, etc.) contain several types of shot transitions (gradual, cut), and to detect them with higher accuracy, an efficient SBD method is needed. Previous researchers have proposed methods for SBD using supervised machine learning approaches; however, data training is a limitation for them. Some previous papers have proposed unsupervised approaches; however, detection for gradual transition shot boundaries has not achieved good accuracy. This paper proposes a distance-calculating algorithm for SBD using principal component analysis (PCA) features and then a deep learning method to improve the detection precision of gradual transition shot boundary frames and normal frames. Firstly, PCA is used for feature extraction of the video frames, and the most significant eigenvectors and eigenvalues are analyzed. Secondly, a distance-calculating algorithm is used on adjacent video frames depending on their eigenvectors and eigenvalues to determine the shot boundaries. Finally, to improve detection accuracy, false detection boundaries are reviewed and classified using CNN. Experimental results show the efficiency of the proposed method
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2021
Modified:
2026-04-20
Issued:
2026-04-20
บทความ/Article
application/pdf
BibliograpyCitation :
In Chiang Mai University. Faculty of Engineering. 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2021) (pp.272-275). Chiang Mai : Chiang Mai University
eng
©copyrights King Mongkut's University of Technology North Bangkok
RightsAccess:
ใช้เวลา
0.041072 วินาที
Dipanita Chakraborty
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