Exploiting the dependencies in information fusion

Hao Pan, Zhi Pei Liang, Thomas S. Huang

Research output: Contribution to journalConference article

Abstract

This paper presents a novel approach for multi-sensory information fusion in the Bayesian inference framework. Specifically, under the maximum entropy principle, a formula is derived for estimating the joint probabilities of multisensory signals. The formula uses appropriate mapping functions to reflect the dependencies among multisensory signals. Selection of the mappings is guided by the maximum mutual information criterion. In addition, an algorithm is proposed for linear mappings of Gaussian random variables. Experiments on simulated Gaussian data and video/audio signals have been carried out. Preliminary results demonstrate that the proposed method can significantly improve the recognition accuracy for this type of tasks.

Original languageEnglish (US)
Pages (from-to)407-412
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'99) - Fort Collins, CO, USA
Duration: Jun 23 1999Jun 25 1999

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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