基于本体的多模信息融合方法

Chunjiang Zhao, Huarui Wu, Ronghua Gao
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引用次数: 0

摘要

为了更好地利用具有海量、异构、多模等特点的信息,消除冗余,形成系统环境相对完整和一致的描述。它可以确保快速、正确的决策、计划和反思。针对分层结构的缺点,提出了一种多模式信息融合方法。可以屏蔽不同的数据库和不同格式之间的数据文件,而模糊神经网络算法通过不断学习,使文本、图像和视频无缝融合。实验结果表明,该方法在正确速率、泄漏检出率和误接受率方面都优于现有的融合算法。
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Ontology-based multimode information fusion method
In order to better use with massive, heterogeneous, multimode and other characteristics of information, eliminate redundancy, the formation of the system environment is relatively complete and consistent description. It can ensure rapid and correct decision-making, planning and reflection. In this paper, a multimode information fusion method is proposed aimed at the shortcomings of hierarchical structure. Different databases and data files between different formats can be shielded, while the fuzzy neural network algorithm, make text, images and video seamless fusion by continuous learning. Experimental results show that the method of this paper is better than exist on fusion algorithms at the right rate, leakage pick up rate and false acceptance rata.
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