NDLI: Uncertainty Reasoning Based Formal Framework for Big Video Data Understanding

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Uncertainty Reasoning Based Formal Framework for Big Video Data Understanding

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WI 2014 - II Author Index

WI 2014 - II Publisher's Information

Uncertainty Reasoning Based Formal Framework for Big Video Data Understanding

Content Provider	ACM Digital Library
Author	Clawson, Kathy Chen, Shuwei Liu, Jun Scotney, Bryan Wang, Hui Jing, Min
Abstract	It is worthwhile to incorporate human knowledge with conventional machine learning approaches for big data analytics. Focusing on big video data understanding, this paper presents a formal scenario recognition framework where knowledge-based logic representation and reasoning is combined with data-based learning approach to enhance scenario recognition capabilities. This is achieved via multi-layered (hierarchical) processing. This approach constructs the hierarchical representation structure based on the semantic understanding of considered scenario, and transforms the structure into logic formulas. After applying conventional computer vision methods for low-level events classification, we apply logic based uncertainty reasoning to determine scene content. Experimental results on a benchmark dataset are provided to show the rationality of the proposed approach.
Starting Page	487
Ending Page	494
Page Count	8
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.138
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Video data understanding scenario recognition formal logical representation hierarchical structure uncertainty reasoning
Content Type	Text
Resource Type	Article

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