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Image Mining Using Directional Spatial Constraints Selim Aksoy, Member, IEEE and R. Gokberk Cinbis
 

Summary: 1
Image Mining Using Directional Spatial Constraints
Selim Aksoy, Member, IEEE and R. G¨okberk Cinbis¸
Abstract--Spatial information plays a fundamental role in
building high-level content models for supporting analysts' inter-
pretations and automating geospatial intelligence. We describe a
framework for modeling directional spatial relationships among
objects and using this information for contextual classification
and retrieval. The proposed model first identifies image areas
that have a high degree of satisfaction of a spatial relation with
respect to several reference objects. Then, this information is
incorporated into the Bayesian decision rule as spatial priors
for contextual classification. The model also supports dynamic
queries by using directional relationships as spatial constraints
to enable object detection based on the properties of individual
objects as well as their spatial relationships to other objects.
Comparative experiments using high-resolution satellite imagery
illustrate the flexibility and effectiveness of the proposed frame-
work in image mining with significant improvements in both
classification and retrieval performance.

  

Source: Aksoy, Selim - Department of Computer Engineering, Bilkent University

 

Collections: Computer Technologies and Information Sciences