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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decision Fusion
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach
Optimal multimodal fusion for multimedia data analysis
Proceedings of the 12th annual ACM international conference on Multimedia
Probabilistic optimized ranking for multimedia semantic concept detection via RVM
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
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
Multimedia Evidence Fusion for Video Concept Detection via OWA Operator
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
Up-fusion: an evolving multimedia decision fusion method
MM '11 Proceedings of the 19th ACM international conference on Multimedia
| Hi-index | 0.00 |
The number of multimedia applications has been increasing over the past two decades. Multimedia information fusion has therefore attracted significant attention with many techniques having been proposed. However, the uncertainty and correlation among different modalities have not been fully considered in the existing fusion methods. In general, the predictions of individual modality have uncertainty, furthermore, many modalities are correlated with each other. In this paper, we propose a novel multimedia fusion method based on the Portfolio theory. Portfolio theory is a widely used financial investment theory dealing with how to allocate funds across assets. The key idea is to maximize the performance of the allocated portfolio while minimize the risk in returns. We adapt this approach to multimodal fusion to derive optimal weights that can achieve good fusion results. The optimization is formulated as a quadratic programming problem. Experimental results with both simulated data and real data confirm the theoretical insights and show promising results.