Volume 2, Issue 1, March 2009, Pages 39 - 50
Video Classification and Shot Detection for Video Retrieval Applications
- M. K. Geetha, S. Palanivel
Available Online 1 March 2009.
- https://doi.org/10.2991/jnmp.2009.2.1.5How to use a DOI?
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - M. K. Geetha AU - S. Palanivel PY - 2009 DA - 2009/03 TI - Video Classification and Shot Detection for Video Retrieval Applications JO - International Journal of Computational Intelligence Systems SP - 39 EP - 50 VL - 2 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/jnmp.2009.2.1.5 DO - https://doi.org/10.2991/jnmp.2009.2.1.5 ID - Geetha2009 ER -