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 |
| Summary: | 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. |
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| ISSN: | 1875-6883 |