Learning bayesian classifiers for scene classification with a visual grammar

Date
2005
Advisor
Instructor
Source Title
Advances in Techniques for Analysis of Remotely Sensed Data
Print ISSN
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
212 - 218
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
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.

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Book Title
Keywords
Bayesian methods, Layout, Prototypes, NASA, Remote sensing, Image analysis, Pixel, Image segmentation, Image retrieval, Postal services
Citation
Published Version (Please cite this version)