Semantic Adaptation of Neural Network Classifiers in Image Segmentation (original) (raw)

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.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, 15780, Greece
    Nikolaos Simou, Thanos Athanasiadis, Stefanos Kollias, Giorgos Stamou & Andreas Stafylopatis

Authors

  1. Nikolaos Simou
  2. Thanos Athanasiadis
  3. Stefanos Kollias
  4. Giorgos Stamou
  5. Andreas Stafylopatis

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Simou, N., Athanasiadis, T., Kollias, S., Stamou, G., Stafylopatis, A. (2008). Semantic Adaptation of Neural Network Classifiers in Image Segmentation. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9\_93

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