Texture based segmentation using graph cut and Gabor filters

This paper describes a method for texture based segmentation. Texture features are extracted by applying a bank of Gabor filters using two-sided convolution strategy. Probability texture model is represented by Gaussian mixture that is trained with the Expectation-maximization algorithm. Texture sim...

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Bibliographic Details
Published in:Pattern recognition and image analysis 2011-06, Vol.21 (2), p.258-261
Main Authors: Jirik, M., Ryba, T., Zelezny, M.
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Analysis and Understanding of Images
Computer Science
Gabor filters
Graph theory
Graphs
Image Processing and Computer Vision
Image processing systems
Labeling
Mathematical models
Neighborhoods
Pattern Recognition
Processing
Representation
Segmentation
Similarity
Strategy
Studies
Surface layer
Texture
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Summary:This paper describes a method for texture based segmentation. Texture features are extracted by applying a bank of Gabor filters using two-sided convolution strategy. Probability texture model is represented by Gaussian mixture that is trained with the Expectation-maximization algorithm. Texture similarity, obtained this way, is used like the input of a Graph cut method. We show that the combination of texture analysis and the Graph cut method produce good results.
ISSN:1054-6618
1555-6212
DOI:10.1134/S105466181102043X