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Learning Bayesian Classifiers for Scene Classification with a Visual Grammar
 

Summary: 1
Learning Bayesian Classifiers for Scene
Classification with a Visual Grammar
Selim Aksoy, Member, IEEE, Krzysztof Koperski, Member, IEEE, Carsten Tusk,
Giovanni Marchisio, Member, IEEE, James C. Tilton, Senior Member, IEEE
Abstract-- A challenging problem in image content extraction
and classification is building a system that automatically learns
high-level semantic interpretations of images. We describe a
Bayesian framework for a visual grammar that aims to reduce
the gap between low-level features and high-level user semantics.
Our approach includes modeling image pixels using automatic
fusion of their spectral, textural and other ancillary attributes;
segmentation of image regions using an iterative split-and-merge
algorithm; and representing scenes by decomposing them into
prototype regions and modeling the interactions between these
regions in terms of their spatial relationships. Naive Bayes clas-
sifiers are used in the learning of models for region segmentation
and classification using positive and negative examples for user-
defined semantic land cover labels. The system also automatically
learns representative region groups that can distinguish different

  

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

 

Collections: Computer Technologies and Information Sciences