Feature relationships hypergraph for multimodal recognition

Authors:
Luming Zhang;Mingli Song;Wei Bian;Dacheng Tao;Xiao Liu;Jiajun Bu;Chun Chen
Affiliations:
Zhejiang Provincial Key Laboratory of Service Robot, Computer Science College, Zhejiang University, China;Zhejiang Provincial Key Laboratory of Service Robot, Computer Science College, Zhejiang University, China;Centre for Quantum Computation and Information Systems, University of Technology, Sydney, Australia;Centre for Quantum Computation and Information Systems, University of Technology, Sydney, Australia;Zhejiang Provincial Key Laboratory of Service Robot, Computer Science College, Zhejiang University, China;Zhejiang Provincial Key Laboratory of Service Robot, Computer Science College, Zhejiang University, China;Zhejiang Provincial Key Laboratory of Service Robot, Computer Science College, Zhejiang University, China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

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Abstract

Utilizing multimodal features to describe multimedia data is a natural way for accurate pattern recognition. However, how to deal with the complex relationships caused by the tremendous multimodal features and the curse of dimensionality are still two crucial challenges. To solve the two problems, a new multimodal features integration method is proposed. Firstly, a so-called Feature Relationships Hypergraph (FRH) is proposed to model the high-order correlations among the multimodal features. Then, based on FRH, the multimodal features are clustered into a set of low-dimensional partitions. And two types of matrices, the inter-partition matrix and intra-partition matrix, are computed to quantify the inter- and intra- partition relationships. Finally, a multi-class boosting strategy is developed to obtain a strong classifier by combining the weak classifiers learned from the intra- partition matrices. The experimental results on different datasets validate the effectiveness of our approach.