Volume 2, Issue 1, March 2009, Pages 39 - 50
Video Classification and Shot Detection for Video Retrieval Applications
Authors
M. Kalaiselvi Geetha, S. Palanivel
Corresponding Author
M. Kalaiselvi Geetha
Received 21 July 2008, Revised 10 February 2009, Available Online 1 March 2009.
- DOI
- 10.2991/jnmp.2009.2.1.5How to use a DOI?
- Abstract
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.
- Copyright
- © 2009, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - M. Kalaiselvi Geetha AU - S. Palanivel PY - 2009 DA - 2009/03/01 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 - 10.2991/jnmp.2009.2.1.5 ID - Geetha2009 ER -