Neural Network Combining Classifier Based on Dempster-Shafer Theory

In this paper, we propose an improved version of RBF network based on Evidence Theory (NNET) using one input layer and two hidden layers and one output layer, to improve classifier combination and recognition reliability in particular for automatic semantic-based video content indexing and retrieval. Many combination schemes have been proposed in the literature according to the type of information provided by each classifier as well as their training and adaptation abilities. Experiments are conducted in the framework of the TrecVid 2005 features extraction task that consists in ordering shots with respect to their relevance to a given class. Finally, we show the efficiency of NN-ET combination method.