NDLI: Learning qualitative relations in real world scenes

Content Provider	IEEE Xplore Digital Library
Author	Ranasinghe, D.D.M. Karunananda, A.S. Ratnayake, U.
Copyright Year	2008
Description	Author affiliation: Dept. of Math.&Comput. Sci., Open Univ., Nugegoda (Ranasinghe, D.D.M.) \|\| Univ. of Moratuwa, Moratuwa (Karunananda, A.S.) \|\| Dept. of Electr. & Comput. Eng., Open Univ., Nugegoda (Ratnayake, U.)
Abstract	Learning from visual scenes is an innate ability of human beings yet an unresolved and highly complex task for machines and is being addressed in the area of cognitive vision systems. Most of the present developments in cognitive vision systems adopt quantitative or model base approaches. Quantitative approaches involve tedious calculations and yield black box type of learning while in model base approaches the structure of the model has to be known before hand, which is not practical in all situations. Therefore, this research work primarily adopts a qualitative approach in learning from visual scenes and inductive logic programming is used to learn rules of a visual scene from a pool of example scenes. The prototype system developed is capable of learning accurate rules, which in turn used for learning new models and the system is capable of learning beyond initial specifications.
Starting Page	413
Ending Page	418
File Size	242161
Page Count	6
File Format	PDF
ISBN	9781424428991
DOI	10.1109/ICIAFS.2008.4783982
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-12
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Logic programming Humans Mathematics cognitive vision systems Information technology temporal reasoning Computer science Machine vision Layout Prototypes Robustness qualitative spatial reasoning inductive logic programming
Content Type	Text
Resource Type	Article

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