Unsupervised textured image segmentation

George K. Gregoriou; Oleh J. Tretiak

doi:10.1109/ICASSP.1992.226273

Unsupervised textured image segmentation

George K. Gregoriou, Oleh J. Tretiak

Department of Engineering

Drexel University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A new algorithm for unsupervised textured image segmentation is presented. The image is comprised of M textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is reduced to one of restoring a vectorvalued underlying field embedded in additive Gaussian noise. The vector means corresponding to the different regions are estimated by using the EM algorithm. By using these means an iterative algorithm is employed, where the underlying field is modeled as a multi-level logistic Markov random field. The results obtained on 2-region and 4-region textured images are impressive and the classification error is less than 3%. The algorithm is not limited to textured images but can also be applied to any vector-valued signals.

Original language	English
Title of host publication	ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	73-76
Number of pages	4
Volume	3
ISBN (Electronic)	0780305329
DOIs	https://doi.org/10.1109/ICASSP.1992.226273
Publication status	Published - 1992
Event	1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 - San Francisco, United States Duration: 23 Mar 1992 → 26 Mar 1992

Other

Other	1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
Country/Territory	United States
City	San Francisco
Period	23/03/92 → 26/03/92

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10.1109/ICASSP.1992.226273

Cite this

@inproceedings{3b98c09ff60848618187c5a8c822ce7d,

title = "Unsupervised textured image segmentation",

abstract = "A new algorithm for unsupervised textured image segmentation is presented. The image is comprised of M textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is reduced to one of restoring a vectorvalued underlying field embedded in additive Gaussian noise. The vector means corresponding to the different regions are estimated by using the EM algorithm. By using these means an iterative algorithm is employed, where the underlying field is modeled as a multi-level logistic Markov random field. The results obtained on 2-region and 4-region textured images are impressive and the classification error is less than 3%. The algorithm is not limited to textured images but can also be applied to any vector-valued signals.",

author = "Gregoriou, {George K.} and Tretiak, {Oleh J.}",

year = "1992",

doi = "10.1109/ICASSP.1992.226273",

language = "English",

volume = "3",

pages = "73--76",

booktitle = "ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 ; Conference date: 23-03-1992 Through 26-03-1992",

}

Gregoriou, GK & Tretiak, OJ 1992, Unsupervised textured image segmentation. in ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing. vol. 3, 226273, Institute of Electrical and Electronics Engineers Inc., pp. 73-76, 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992, San Francisco, United States, 23/03/92. https://doi.org/10.1109/ICASSP.1992.226273

TY - GEN

T1 - Unsupervised textured image segmentation

AU - Gregoriou, George K.

AU - Tretiak, Oleh J.

PY - 1992

Y1 - 1992

N2 - A new algorithm for unsupervised textured image segmentation is presented. The image is comprised of M textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is reduced to one of restoring a vectorvalued underlying field embedded in additive Gaussian noise. The vector means corresponding to the different regions are estimated by using the EM algorithm. By using these means an iterative algorithm is employed, where the underlying field is modeled as a multi-level logistic Markov random field. The results obtained on 2-region and 4-region textured images are impressive and the classification error is less than 3%. The algorithm is not limited to textured images but can also be applied to any vector-valued signals.

AB - A new algorithm for unsupervised textured image segmentation is presented. The image is comprised of M textured regions, each of which is modeled by a stationary Gaussian Markov random field. A feature vector is computed for each pixel in the original image where these vectors are normally distributed and cluster about some vector means. Thus, the problem is reduced to one of restoring a vectorvalued underlying field embedded in additive Gaussian noise. The vector means corresponding to the different regions are estimated by using the EM algorithm. By using these means an iterative algorithm is employed, where the underlying field is modeled as a multi-level logistic Markov random field. The results obtained on 2-region and 4-region textured images are impressive and the classification error is less than 3%. The algorithm is not limited to textured images but can also be applied to any vector-valued signals.

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U2 - 10.1109/ICASSP.1992.226273

DO - 10.1109/ICASSP.1992.226273

M3 - Conference contribution

AN - SCOPUS:85019596568

VL - 3

SP - 73

EP - 76

BT - ICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992

Y2 - 23 March 1992 through 26 March 1992

ER -

Unsupervised textured image segmentation

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