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
Latent semantic analysis for an effective region-based video shot retrieval system
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Adaptive mixtures of local experts
Neural Computation
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Perplexity-based evidential neural network classifier fusion using mpeg-7 low-level visual features
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Evidence Theory-Based Multimodal Emotion Recognition
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Artificial Intelligence in Medicine
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
| Hi-index | 0.00 |
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.