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Communication software and platform develop sketch map and CONTENT and wrap up and step the computer network, such multi-platform information, in order to offer its explanation relying mainly on stepping the platform of reference mainly as railway application,etc
Semantic analysis of multimedia CONTENT is an on going research
area that has gained a lot of attention over the last few years.
Additionally, machine learning techniques are widely used for multimedia
analysis with great success. This work presents a combined approach
to semantic adaptation of neural network classifiers in multimedia framework.
It is based on a fuzzy reasoning engine which is able to evaluate
the outputs and the confidence levels of the neural network classifier, using
a knowledge base. Improved image segmentation results are obtained,
which are used for adaptation of the network classifier, further increasing
its ability to provide accurate classification of the specific CONTENT.
