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      • Department of Computer Engineering
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      Learning bayesian classifiers for scene classification with a visual grammar

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      Author
      Aksoy, Selim
      Koperski, K.
      Tusk, C.
      Marchisio, G.
      Tilton J. C.
      Date
      2005
      Source Title
      Advances in Techniques for Analysis of Remotely Sensed Data
      Publisher
      IEEE
      Pages
      212 - 218
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      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 classifiers 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 scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples. © 2005 IEEE.
      Keywords
      Bayesian methods
      Layout
      Prototypes
      NASA
      Remote sensing
      Image analysis
      Pixel
      Image segmentation
      Image retrieval
      Postal services
      Permalink
      http://hdl.handle.net/11693/27387
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/WARSD.2003.1295195
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      • Department of Computer Engineering 1370
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