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. © Springer-Verlag Berlin Heidelberg 2008.
CITATION STYLE
Simou, N., Athanasiadis, T., Kollias, S., Stamou, G., & Stafylopatis, A. (2008). Semantic adaptation of neural network classifiers in image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5163 LNCS, pp. 907–916). https://doi.org/10.1007/978-3-540-87536-9_93
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