Multi-Modal Visual Features-Based Video Shot Boundary Detection
One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still rem...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7954599/ |
| _version_ | 1818876915794051072 |
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| author | Sawitchaya Tippaya Suchada Sitjongsataporn Tele Tan Masood Mehmood Khan Kosin Chamnongthai |
| author_facet | Sawitchaya Tippaya Suchada Sitjongsataporn Tele Tan Masood Mehmood Khan Kosin Chamnongthai |
| author_sort | Sawitchaya Tippaya |
| collection | DOAJ |
| description | One of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods. |
| first_indexed | 2024-12-19T13:49:59Z |
| format | Article |
| id | doaj.art-f7064bb7a5ad47ebb150f5d9a0bd6900 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:49:59Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f7064bb7a5ad47ebb150f5d9a0bd69002022-12-21T20:18:46ZengIEEEIEEE Access2169-35362017-01-015125631257510.1109/ACCESS.2017.27179987954599Multi-Modal Visual Features-Based Video Shot Boundary DetectionSawitchaya Tippaya0Suchada Sitjongsataporn1Tele Tan2Masood Mehmood Khan3Kosin Chamnongthai4https://orcid.org/0000-0003-1509-5754Department of Electronics and Telecommunication Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandDepartment of Electronic Engineering, Mahanakorn University of Technology, Bangkok, ThailandDepartment of Mechanical Engineering, Faculty of Science and Engineering, Curtin University, Bentley Campus, Perth, WA, AustraliaDepartment of Mechanical Engineering, Faculty of Science and Engineering, Curtin University, Bentley Campus, Perth, WA, AustraliaDepartment of Electronics and Telecommunication Engineering, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandOne of the essential pre-processing steps of semantic video analysis is the video shot boundary detection (SBD). It is the primary step to segment the sequence of video frames into shots. Many SBD systems using supervised learning have been proposed for years; however, the training process still remains its principal limitation. In this paper, a multi-modal visual features-based SBD framework is employed that aims to analyze the behaviors of visual representation in terms of the discontinuity signal. We adopt a candidate segment selection that performs without the threshold calculation but uses the cumulative moving average of the discontinuity signal to identify the position of shot boundaries and neglect the non-boundary video frames. The transition detection is structurally performed to distinguish candidate segment into a cut transition and a gradual transition, including fade in/out and logo occurrence. Experimental results are evaluated using the golf video clips and the TREC2001 documentary video data set. Results show that the proposed SBD framework can achieve good accuracy in both types of video data set compared with other proposed SBD methods.https://ieeexplore.ieee.org/document/7954599/Cut transition detectiongradual transition detectiongolf video analysislogo transition detectiontransition pattern analysisvideo shot boundary detection |
| spellingShingle | Sawitchaya Tippaya Suchada Sitjongsataporn Tele Tan Masood Mehmood Khan Kosin Chamnongthai Multi-Modal Visual Features-Based Video Shot Boundary Detection IEEE Access Cut transition detection gradual transition detection golf video analysis logo transition detection transition pattern analysis video shot boundary detection |
| title | Multi-Modal Visual Features-Based Video Shot Boundary Detection |
| title_full | Multi-Modal Visual Features-Based Video Shot Boundary Detection |
| title_fullStr | Multi-Modal Visual Features-Based Video Shot Boundary Detection |
| title_full_unstemmed | Multi-Modal Visual Features-Based Video Shot Boundary Detection |
| title_short | Multi-Modal Visual Features-Based Video Shot Boundary Detection |
| title_sort | multi modal visual features based video shot boundary detection |
| topic | Cut transition detection gradual transition detection golf video analysis logo transition detection transition pattern analysis video shot boundary detection |
| url | https://ieeexplore.ieee.org/document/7954599/ |
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