Triplet Markov Trees for Image Segmentation

Jean Baptiste Courbot; Emmanuel Monfrini; Vincent Mazet; Christophe Collet

doi:10.1109/SSP.2018.8450841

Triplet Markov Trees for Image Segmentation

Jean Baptiste Courbot
, Emmanuel Monfrini
, Vincent Mazet
, Christophe Collet

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

Abstract

This paper introduces a triplet Markov tree model designed to minimize the block effect that may be encountered while segmenting image using Hidden Markov Tree (HMT) modeling. We present the model specificities, the Bayesian Maximum Posterior Mode segmentation, and a parameter estimation strategy in the unsupervised context. Results on synthetic images show that the method greatly improves over HMTbased segmentation, and that the model is competitive with a hidden Markov field-based segmentation.

Original language	English
Title of host publication	2018 IEEE Statistical Signal Processing Workshop, SSP 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	628-632
Number of pages	5
ISBN (Print)	9781538615706
DOIs	https://doi.org/10.1109/SSP.2018.8450841
Publication status	Published - 29 Aug 2018
Event	20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany Duration: 10 Jun 2018 → 13 Jun 2018

Publication series

Name	2018 IEEE Statistical Signal Processing Workshop, SSP 2018

Conference

Conference	20th IEEE Statistical Signal Processing Workshop, SSP 2018
Country/Territory	Germany
City	Freiburg im Breisgau
Period	10/06/18 → 13/06/18

Keywords

Image Segmentation
Triplet Markov Tree
Unsupervised segmentation

Access to Document

10.1109/SSP.2018.8450841

Cite this

@inproceedings{ce6f306b4302428483cd6f5f02cd3d7e,

title = "Triplet Markov Trees for Image Segmentation",

abstract = "This paper introduces a triplet Markov tree model designed to minimize the block effect that may be encountered while segmenting image using Hidden Markov Tree (HMT) modeling. We present the model specificities, the Bayesian Maximum Posterior Mode segmentation, and a parameter estimation strategy in the unsupervised context. Results on synthetic images show that the method greatly improves over HMTbased segmentation, and that the model is competitive with a hidden Markov field-based segmentation.",

keywords = "Image Segmentation, Triplet Markov Tree, Unsupervised segmentation",

author = "Courbot, \{Jean Baptiste\} and Emmanuel Monfrini and Vincent Mazet and Christophe Collet",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 20th IEEE Statistical Signal Processing Workshop, SSP 2018 ; Conference date: 10-06-2018 Through 13-06-2018",

year = "2018",

month = aug,

day = "29",

doi = "10.1109/SSP.2018.8450841",

language = "English",

isbn = "9781538615706",

series = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",

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

pages = "628--632",

booktitle = "2018 IEEE Statistical Signal Processing Workshop, SSP 2018",

}

Courbot, JB, Monfrini, E, Mazet, V & Collet, C 2018, Triplet Markov Trees for Image Segmentation. in 2018 IEEE Statistical Signal Processing Workshop, SSP 2018., 8450841, 2018 IEEE Statistical Signal Processing Workshop, SSP 2018, Institute of Electrical and Electronics Engineers Inc., pp. 628-632, 20th IEEE Statistical Signal Processing Workshop, SSP 2018, Freiburg im Breisgau, Germany, 10/06/18. https://doi.org/10.1109/SSP.2018.8450841

Triplet Markov Trees for Image Segmentation. / Courbot, Jean Baptiste; Monfrini, Emmanuel; Mazet, Vincent et al.
2018 IEEE Statistical Signal Processing Workshop, SSP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 628-632 8450841 (2018 IEEE Statistical Signal Processing Workshop, SSP 2018).

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

TY - GEN

T1 - Triplet Markov Trees for Image Segmentation

AU - Courbot, Jean Baptiste

AU - Monfrini, Emmanuel

AU - Mazet, Vincent

AU - Collet, Christophe

PY - 2018/8/29

Y1 - 2018/8/29

N2 - This paper introduces a triplet Markov tree model designed to minimize the block effect that may be encountered while segmenting image using Hidden Markov Tree (HMT) modeling. We present the model specificities, the Bayesian Maximum Posterior Mode segmentation, and a parameter estimation strategy in the unsupervised context. Results on synthetic images show that the method greatly improves over HMTbased segmentation, and that the model is competitive with a hidden Markov field-based segmentation.

AB - This paper introduces a triplet Markov tree model designed to minimize the block effect that may be encountered while segmenting image using Hidden Markov Tree (HMT) modeling. We present the model specificities, the Bayesian Maximum Posterior Mode segmentation, and a parameter estimation strategy in the unsupervised context. Results on synthetic images show that the method greatly improves over HMTbased segmentation, and that the model is competitive with a hidden Markov field-based segmentation.

KW - Image Segmentation

KW - Triplet Markov Tree

KW - Unsupervised segmentation

UR - https://www.scopus.com/pages/publications/85053816358

U2 - 10.1109/SSP.2018.8450841

DO - 10.1109/SSP.2018.8450841

M3 - Conference contribution

AN - SCOPUS:85053816358

SN - 9781538615706

T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

SP - 628

EP - 632

BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018

Y2 - 10 June 2018 through 13 June 2018

ER -

Triplet Markov Trees for Image Segmentation

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Keywords

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