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 fr a mework.

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.