Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/WI-IAT.2014.138 | WI-IAT |
Keywords | Field | DocType |
formal scenario recognition framework,knowledge-based reasoning,algorithms,design,video data understanding, scenario recognition, formal logical representation, hierarchical structure, uncertainty reasoning,knowledge based systems,experimentation,big data,video data understanding,multilayered processing,big video data understanding,learning (artificial intelligence),scenario recognition capabilities,uncertainty, fuzzy, and probabilistic reasoning,pattern classification,data analysis,low-level events classification,hierarchical structure,scenario recognition,measurement,data-based learning approach,uncertainty handling,world wide web,knowledge-based logic representation,uncertainty reasoning,formal logical representation,computer vision methods,vision and scene understanding,performance,hierarchical processing,logic based uncertainty reasoning,cognition,optical imaging,computing,semantics,uncertainty,vectors,computer vision | Rationality,Computer science,Human knowledge,Artificial intelligence,Cognition,Big data,Optical imaging,Semantics,Machine learning | Conference |
Volume | ISBN | Citations |
2 | 978-1-4799-4143-8-02 | 2 |
PageRank | References | Authors |
0.36 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuwei Chen | 1 | 121 | 12.14 |
| Kathy M. Clawson | 2 | 9 | 1.83 |
| Min Jing | 3 | 14 | 3.09 |
| Jun Liu | 4 | 644 | 56.21 |
| Hui Wang | 5 | 257 | 24.41 |
| Bryan W. Scotney | 6 | 670 | 82.50 |