Classifier Fusion: Combination Methods For Semantic Indexing in Video Content

Classifier combination has been investigated as a new research field to improve recognition reliability by taking into account the complementarity between classifiers, 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. This paper presents an overview of current research in classifier combination and a comparative study of a number of combination methods. A novel training technique called Weighted Ten Folding based on Ten Folding principle is proposed for combining classifier. 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 different combination methods.

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