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Performance Modeling and Algorithm Characterization for Robust Image Segmentation

Authors
  • Shah, Shishir K.1, 2
  • 1 University of Houston, Quantitative Imaging Laboratory, Houston, TX, 77204-3010, USA , Houston (United States)
  • 2 University of Houston, Department of Computer Science, Houston, TX, 77204-3010, USA , Houston (United States)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Mar 20, 2008
Volume
80
Issue
1
Pages
92–103
Identifiers
DOI: 10.1007/s11263-008-0130-z
Source
Springer Nature
Keywords
License
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Abstract

This paper presents a probabilistic framework based on Bayesian theory for the performance prediction and selection of an optimal segmentation algorithm. The framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm. The input image information content is measured in terms of image features while the candidate segmentation algorithm’s behavioral characteristics are captured through the use of segmentation quality features. Gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the Berkeley Segmentation Dataset using four candidate segmentation algorithms.

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