International Journal of Computational Intelligence Systems

Volume 2, Issue 1, March 2009, Pages 39 - 50

Video Classification and Shot Detection for Video Retrieval Applications

Authors
M. K. Geetha, S. Palanivel
Available Online 1 March 2009.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
2 - 1
Pages
39 - 50
Publication Date
2009/03
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/jnmp.2009.2.1.5How to use a DOI?
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  -