A theory of multiple classifier systems and its application to visual word recognition
A theory of multiple classifier systems and its application to visual word recognition
Blobworld: A System for Region-Based Image Indexing and Retrieval
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Adaptive mixtures of local experts
Neural Computation
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In this paper we compare a number of classifier fusion approaches within a complete and efficient framework for video shot indexing and retrieval. The aim of the fusion stage of our sytem is to detect the semantic content of video shots based on classifiers output obtained from low level features. An overview of current research in classifier fusion is provided along with a comparative study of four combination methods. A novel training technique called Weighted Ten Folding based on Ten Folding principle is proposed for combining classifier. The experimental results conducted in the framework of the TrecVid'05 features extraction task report the efficiency of different combination methods and show the improvement provided by our proposed scheme.