The Strength of Weak Learnability
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Decision Fusion
Video Retrieval by Feature Learning in Key Frames
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Boosting Image Orientation Detection with Indoor vs. Outdoor Classification
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Boosting contextual information in content-based image retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Classifier Fusion Using Shared Sampling Distribution for Boosting
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Audio-visual synchrony for detection of monologues in video archives
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
3D+2D Face Localization Using Boosting in Multi-Modal Feature Space
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Semantics reinforcement and fusion learning for multimedia streams
Proceedings of the 6th ACM international conference on Image and video retrieval
AdaBoost Multiple Feature Selection and Combination for Face Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Concept detectors: how good is good enough?
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Content-based copy detection through multimodal feature representation and temporal pyramid matching
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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The multimodal data usually contain complementary, correlated and redundant information. Thus, multimodal fusion is useful for many multisensor applications. Here, a novel multimodal fusion algorithm is proposed, which is referred to as “MultiFusion.” The approach adopts a boosting structure where the atomic event is considered as the fusion unit. The correlation of multimodal data is used to form an overall classifier in each iteration. Moreover, by adopting the Adaboost-like structure, the overall fusion performance is improved. Both the simulation experiment and the real application show the effectiveness of the MultiFusion approach. Our approach can be applied in different multimodal applications to exploit the multimedia data characteristics and improve the performance.