NDLI: Data mining and spatial reasoning for satellite image characterization

Content Provider	IEEE Xplore Digital Library
Author	Vaduva, C. Faur, D. Gavat, I.
Copyright Year	2010
Description	Author affiliation: Applied Electronics and Information Technology Department, University Politehnica of Bucharest, Romania (Vaduva, C.; Faur, D.; Gavat, I.)
Abstract	High level image understanding and content extraction requires image regions analysis to reveal the spatial interaction between them. This paper aims to engender new attributes for scene description considering the relative position of the objects inside. A visual grammar of the scene is built using an extension for a Knowledge Based Image Information Mining system (KIM). The objects are extracted using statistical models and machine learning through the KIM system, according to the user interest. Further, an affine invariant descriptor of the relative position between two objects is computed. This is the force histogram and it is considered to be a spatial signature which characterizes configurations of regions based on the attraction forces between the composing objects. Thereby, new patterns could be defined using similar object configurations, in order to enhance the effectiveness of the content-based image retrieval inside large databases.
Starting Page	173
Ending Page	176
File Size	1298630
Page Count	4
File Format	PDF
ISBN	9781424463602
e-ISBN	9781424463633
DOI	10.1109/ICCOMM.2010.5509002
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-10
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Content based retrieval Image retrieval Information retrieval Spatial relationships Data mining Invariant signatures Histograms Satellites Image analysis High levei image understanding Image databases Layout Machine learning
Content Type	Text
Resource Type	Article

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