Perplexity-based evidential neural network classifier fusion using MPEG-7 low-level visual features

In this paper, an automatic content-based video shot index- ing framework is proposed employing five types of MPEG-7 low-level visual features (color, texture, shape, motion and face). Once the set of features representing the video content is determined, the question of how to combine their individ- ual classifier outputs according to each feature to form a final semantic decision of the shot must be addressed, in the goal of bridging the semantic gap between the low level visual feature and the high level semantic concepts. For this aim, a novel approach called "perplexity-based weighted descrip- tors" is proposed before applying our evidential combiner NNET [3], to obtain an adaptive classifier fusion PENN (Perplexity-based Evidential Neural Network). The experi- mental results conducted in the framework of the TRECVid'07 high level features extraction task report the efficiency and the improvement provided by the proposed scheme.