2009 IEEE International Conference on
Systems, Man, and Cybernetics |
Abstract
With explosive amounts of video data emerging
from the Internet, automatic video concept detection is becoming
very important and has been received great attention. However,
reported approaches mainly suffer from low identification accuracy
and poor robustness over different concepts. One of the
main reason is that the existing approaches typically isolate
the video signature generation from the process of classifier
training. Also, very few approaches consider effects of multiple
video features. The paper describes a novel approach fusing
different information from diverse knowledge sources to facilitate
effective video concept detection. The system is designed based
on CM*F scheme [7], [5] and its basic architecture contains
two core components including 1) CM*F based video signature
generation scheme and 2) CM*F based video concept detector.
To evaluate the approach proposed, an extensive experimental
study on two large video databases has been carried out. The
results demonstrate the superiority of the method in terms of
effectiveness and robustness.