Image, Video and Multimedia Systems Lab
School of Electrical and Computer Engineering,
National Technical University of Athens
Contact person: Stefanos Kollias

The research work of this group that is related to learning and neural networks is focused on supervised neural network training, on-line training and hybrid fuzzy-neural network training with applications to semantic multimedia analysis, coding, search and retrieval, human computer interaction and multimodal emotion/affective recognition.

Supervised Learning, Neural Networks

In [Kol88] the Levenberg-Marquardt algorithm has been proposed for supervised training of feedforward neural networks. This method is very effective compared to typical gradient descent and has been successfully used in many applications. It is now in wide use since it is included in Matlab neural network toolbox.

In [Del94] third order neural networks are proposed for affine invariant recognition of images that based on third order signal (image) statistics (cumulants). Also in [Kol96] multiresolution neural networks are proposed for image recognition based on wavelet decomposition and training of the different scales through a constructive approach.

Context-adaptable supervised neural networks are presented in [Dou00] along with on-line training algorithms that have been successfully used in image analysis and other applications.

Adaptive neurofuzzy models and learning algorithms for semantic multimedia analysis are presented in [Sta05, Ath07, Sto06] while in [Ioa05, Car08] such hybrid models are trained for emotion recognition.