Semantic Adaptation of Neural Network Classifiers in Image Segmentation

Authors:
Nikolaos Simou;Thanos Athanasiadis;Stefanos Kollias;Giorgos Stamou;Andreas Stafylopatis
Affiliations:
Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

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Abstract

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.