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.
