Video Classification and Shot Detection for Video Retrieval Applications
Appropriate organization of video databases is essential for pertinent indexing and retrieval of visual information. This paper proposes a new feature called Block Intensity Comparison Code (BICC) for video classification and an unsupervised shot change detection algorithm to detect the shot changes...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2009-03-01
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| Series: | International Journal of Computational Intelligence Systems |
| Online Access: | https://www.atlantis-press.com/article/1823.pdf |
| _version_ | 1811234207039488000 |
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| author | M. K. Geetha S. Palanivel |
| author_facet | M. K. Geetha S. Palanivel |
| author_sort | M. K. Geetha |
| collection | DOAJ |
| description | Appropriate organization of video databases is essential for pertinent indexing and retrieval of visual information. This paper proposes a new feature called Block Intensity Comparison Code (BICC) for video classification and an unsupervised shot change detection algorithm to detect the shot changes in a video stream using autoassociative neural network (AANN) which makes retrieval problems much simpler. BICC represents the average block intensity difference between blocks of a frame. A novel AANN misclustering rate (AMR) algorithm is used to detect the shot transitions. The experiments demonstrate the effectiveness of the proposed methods. |
| first_indexed | 2024-04-12T11:32:47Z |
| format | Article |
| id | doaj.art-1982c78122e549ff87bed37a0e0491a4 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-12T11:32:47Z |
| publishDate | 2009-03-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-1982c78122e549ff87bed37a0e0491a42022-12-22T03:34:57ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832009-03-012110.2991/jnmp.2009.2.1.5Video Classification and Shot Detection for Video Retrieval ApplicationsM. K. GeethaS. PalanivelAppropriate organization of video databases is essential for pertinent indexing and retrieval of visual information. This paper proposes a new feature called Block Intensity Comparison Code (BICC) for video classification and an unsupervised shot change detection algorithm to detect the shot changes in a video stream using autoassociative neural network (AANN) which makes retrieval problems much simpler. BICC represents the average block intensity difference between blocks of a frame. A novel AANN misclustering rate (AMR) algorithm is used to detect the shot transitions. The experiments demonstrate the effectiveness of the proposed methods.https://www.atlantis-press.com/article/1823.pdf |
| spellingShingle | M. K. Geetha S. Palanivel Video Classification and Shot Detection for Video Retrieval Applications International Journal of Computational Intelligence Systems |
| title | Video Classification and Shot Detection for Video Retrieval Applications |
| title_full | Video Classification and Shot Detection for Video Retrieval Applications |
| title_fullStr | Video Classification and Shot Detection for Video Retrieval Applications |
| title_full_unstemmed | Video Classification and Shot Detection for Video Retrieval Applications |
| title_short | Video Classification and Shot Detection for Video Retrieval Applications |
| title_sort | video classification and shot detection for video retrieval applications |
| url | https://www.atlantis-press.com/article/1823.pdf |
| work_keys_str_mv | AT mkgeetha videoclassificationandshotdetectionforvideoretrievalapplications AT spalanivel videoclassificationandshotdetectionforvideoretrievalapplications |