Multi-modal Visual Features Based Video Shot Boundary Detection
OAPA 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 stil...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
IEEE Access
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/55786 |
| _version_ | 1848759706716209152 |
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| author | Tippaya, S. Sitjongsataporn, S. Tan, Tele Khan, M. Chamnongthai, K. |
| author_facet | Tippaya, S. Sitjongsataporn, S. Tan, Tele Khan, M. Chamnongthai, K. |
| author_sort | Tippaya, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | OAPA 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 analyse the behaviours 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 dataset. Results show that the proposed SBD framework can achieve good accuracy in both types of video dataset compared with other proposed SBD methods. |
| first_indexed | 2025-11-14T10:04:09Z |
| format | Journal Article |
| id | curtin-20.500.11937-55786 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:04:09Z |
| publishDate | 2017 |
| publisher | IEEE Access |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-557862017-09-13T16:11:24Z Multi-modal Visual Features Based Video Shot Boundary Detection Tippaya, S. Sitjongsataporn, S. Tan, Tele Khan, M. Chamnongthai, K. OAPA 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 analyse the behaviours 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 dataset. Results show that the proposed SBD framework can achieve good accuracy in both types of video dataset compared with other proposed SBD methods. 2017 Journal Article http://hdl.handle.net/20.500.11937/55786 10.1109/ACCESS.2017.2717998 IEEE Access restricted |
| spellingShingle | Tippaya, S. Sitjongsataporn, S. Tan, Tele Khan, M. Chamnongthai, K. Multi-modal Visual Features Based 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 |
| url | http://hdl.handle.net/20.500.11937/55786 |