Continuous Behaviour Knowledge Space For Semantic Indexing of Video Content

In this paper we introduce a new method for fusing classifier outputs. It is inspired from the behavior knowledge space model with the extra ability to work on continuous input values. This property allows to deal with heterogeneous classifiers and in particular it does not require to make any decision at the classifier level. We propose to build a set of units, defining a knowledge space, with respect to classifier output spaces. A new sample is then classified with respect to the unit it belongs to and some statistics computed on each unit. Several methods to create cells and make the final decision are proposed and compared to k-nearest neighbor and decision tree schemas. The evaluation is conducted on the task of video content retrieval which will reveal the efficiency of our approach