This paper deals with information retrieval and semantic indexing of multimedia documents. We propose a generic scheme combining an ontologybased evidential framework and high-level multimodal fusion, aimed at recognising semantic concepts in videos. This work is represented on two stages: First, the adaptation of evidence theory to neural network, thus giving Neural Network based on Evidence Theory (NNET). This theory presents two important information for decision-making compared to the probabilistic methods: belief degree and system ignorance. The NNET is then improved further by incorporating the relationship between descriptors and concepts, modeled by a weight vector based on entropy and perplexity. The combination of this vector with the classifiers outputs, gives us a
new model called Perplexity-based Evidential Neural Network (PENN). Secondly, an ontology-based concept is introduced via the influence representation of the relations between concepts and the ontological readjustment of the confidence values. To represent this relationship, three types of information are computed: low-level visual descriptors, concept co-occurrence and semantic similarities. The final system is called Ontological-PENN. A comparison between the main similarity construction methodologies are proposed. Experimental results using the TRECVid dataset are presented to support the effectiveness of our scheme.
