NDLI: Entropy-based information fusion for multimodal data

Content Provider	IEEE Xplore Digital Library
Author	Ping Liang Wongthanavasug, S.
Copyright Year	2014
Description	Author affiliation: Comput. Sci. Dept., Khon Kaen Univ., Khon Kaen, Thailand (Ping Liang; Wongthanavasug, S.)
Abstract	Multimodal data contains a great amount of data in the Internet which hold rich-media content. The fusion of data information is a way to explore the linkage between the Web data in order to integrate the data from heterogeneous sources so that deep information can be extracted. Nowadays Web data are either structured or unstructured and information can be generated from the Web data by supervised or unsupervised methods. The existing methods rely on features generated from histogram data like HMM or pre-defined rules. However, data change in-deterministically at most of time and are hard to pre-define all the states and rules in advance. This paper takes the approach of entropy-based information fusion to treat the information from each source as a stochastic process so that the change of each process can be measured in real time. Then the change of information from each source is integrated and entropy is introduced to measure how far the integrated information of the change is from the best scenario based on histogram data. In such a way, it is possible to deduce an overall inference from data from difference sources in different presentations. Then the inference can be generated automatically with human interference.
Starting Page	296
Ending Page	301
File Size	218463
Page Count	6
File Format	PDF
e-ISBN	9781479949632
DOI	10.1109/ICSEC.2014.6978211
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-30
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Roads Stochastic processes Hidden Markov models Information fusion Multimodal data Entropy Random variables Feeds
Content Type	Text
Resource Type	Article

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