A Markov random field image segmentation model using combined color and texture features

Zoltan Kato; Ting Chuen Pong

doi:10.1007/3-540-44692-3_66

A Markov random field image segmentation model using combined color and texture features

Zoltan Kato, Ting Chuen Pong

Department of Computer Science and Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

24 Citations (Scopus)

Abstract

In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian distributions. Thus, the only hypothesis about the nature of the features is that an additive white noise model is suitable to describe the feature values belonging to a given class. Herein, we use the perceptually uni-form CIE-L*u*v* color values as color features and a set of Gabor filters as texture features. We provide experimental results that illustrate the performance of our method on both synthetic and natural color images. Due to the local nature of our MRF model, the algorithm can be highly parallelized.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns - 9th International Conference, CAIP 2001, Proceedings
Editors	Wladyslaw Skarbek
Publisher	Springer Verlag
Pages	547-554
Number of pages	8
ISBN (Print)	9783540425137
DOIs	https://doi.org/10.1007/3-540-44692-3_66
Publication status	Published - 2001
Event	9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001 - Warsaw, Poland Duration: 5 Sept 2001 → 7 Sept 2001

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2124
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001
Country/Territory	Poland
City	Warsaw
Period	5/09/01 → 7/09/01

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.

Keywords

Image segmentation
Markov random field model

Access to Document

10.1007/3-540-44692-3_66

Cite this

Kato, Z., & Pong, T. C. (2001). A Markov random field image segmentation model using combined color and texture features. In W. Skarbek (Ed.), Computer Analysis of Images and Patterns - 9th International Conference, CAIP 2001, Proceedings (pp. 547-554). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2124). Springer Verlag. https://doi.org/10.1007/3-540-44692-3_66

Kato, Zoltan ; Pong, Ting Chuen. / A Markov random field image segmentation model using combined color and texture features. Computer Analysis of Images and Patterns - 9th International Conference, CAIP 2001, Proceedings. editor / Wladyslaw Skarbek. Springer Verlag, 2001. pp. 547-554 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Markov random field image segmentation model using combined color and texture features",

abstract = "In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian distributions. Thus, the only hypothesis about the nature of the features is that an additive white noise model is suitable to describe the feature values belonging to a given class. Herein, we use the perceptually uni-form CIE-L*u*v* color values as color features and a set of Gabor filters as texture features. We provide experimental results that illustrate the performance of our method on both synthetic and natural color images. Due to the local nature of our MRF model, the algorithm can be highly parallelized.",

keywords = "Image segmentation, Markov random field model",

author = "Zoltan Kato and Pong, \{Ting Chuen\}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001 ; Conference date: 05-09-2001 Through 07-09-2001",

year = "2001",

doi = "10.1007/3-540-44692-3\_66",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Kato, Z & Pong, TC 2001, A Markov random field image segmentation model using combined color and texture features. in W Skarbek (ed.), Computer Analysis of Images and Patterns - 9th International Conference, CAIP 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2124, Springer Verlag, pp. 547-554, 9th International Conference on Computer Analysis of Images and Patterns, CAIP 2001, Warsaw, Poland, 5/09/01. https://doi.org/10.1007/3-540-44692-3_66

A Markov random field image segmentation model using combined color and texture features. / Kato, Zoltan; Pong, Ting Chuen.
Computer Analysis of Images and Patterns - 9th International Conference, CAIP 2001, Proceedings. ed. / Wladyslaw Skarbek. Springer Verlag, 2001. p. 547-554 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2124).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

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N2 - In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian distributions. Thus, the only hypothesis about the nature of the features is that an additive white noise model is suitable to describe the feature values belonging to a given class. Herein, we use the perceptually uni-form CIE-L*u*v* color values as color features and a set of Gabor filters as texture features. We provide experimental results that illustrate the performance of our method on both synthetic and natural color images. Due to the local nature of our MRF model, the algorithm can be highly parallelized.

AB - In this paper, we propose a Markov random field (MRF) image segmentation model which aims at combining color and texture features. The theoretical framework relies on Bayesian estimation associated with combinatorial optimization (Simulated Annealing). The segmentation is obtained by classifying the pixels into different pixel classes. These classes are represented by multi-variate Gaussian distributions. Thus, the only hypothesis about the nature of the features is that an additive white noise model is suitable to describe the feature values belonging to a given class. Herein, we use the perceptually uni-form CIE-L*u*v* color values as color features and a set of Gabor filters as texture features. We provide experimental results that illustrate the performance of our method on both synthetic and natural color images. Due to the local nature of our MRF model, the algorithm can be highly parallelized.

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Kato Z, Pong TC. A Markov random field image segmentation model using combined color and texture features. In Skarbek W, editor, Computer Analysis of Images and Patterns - 9th International Conference, CAIP 2001, Proceedings. Springer Verlag. 2001. p. 547-554. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44692-3_66

A Markov random field image segmentation model using combined color and texture features

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