Performance Modeling and Algorithm Characterization for Robust Image Segmentation Robust Image Segmentation

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 beh...

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Bibliographic Details
Published inInternational journal of computer vision Vol. 80; no. 1; pp. 92 - 103
Main Author Shah, Shishir K.
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.10.2008
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Computer Imaging
Computer Science
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Studies
Vision
Performance modeling
Algorithm selection
Image segmentation
Algorithm characterization
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-008-0130-z