Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern 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
Classifier fusion: combination methods for semantic indexing in video content
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multi-level Fusion for Semantic Video Content Indexing and Retrieval
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
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
Integrating Image Segmentation and Classification for Fuzzy Knowledge-Based Multimedia Indexing
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Evidence Theory-Based Multimodal Emotion Recognition
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Fire detection algorithms for video images of large space structures
Multimedia Tools and Applications
International Journal of Knowledge Discovery in Bioinformatics
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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Classification is a major task in many applications and in particular for automatic semantic-based video content indexing and retrieval. In this paper, we focus on the challenging task of classifier output fusion. It is a necessary step to efficiently estimate the semantic content of video shots from multiple cues. We propose to fuse the numeric information provided by multiple classifiers in the framework of evidence logic. For this purpose, an improved version of RBF network based on Evidence Theory (NN-ET) is proposed. Experiments are conducted in the framework of TrecVid high level feature extraction task that consists of ordering shots with respect to their relevance to a given semantic class.