Bagging stochastic watershed on natural color image segmentation

Gianni Franchi; Jesús Angulo

doi:10.1007/978-3-319-18720-4_36

Bagging stochastic watershed on natural color image segmentation

Gianni Franchi
, Jesús Angulo

Mines ParisTech

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

Abstract

The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochastic approach to improve the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.

Original language	English
Title of host publication	Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings
Editors	Laurent Najman, Hugues Talbot, Jon Atli Benediktsson, Jocelyn Chanussot
Publisher	Springer Verlag
Pages	422-433
Number of pages	12
ISBN (Print)	9783319187198
DOIs	https://doi.org/10.1007/978-3-319-18720-4_36
Publication status	Published - 1 Jan 2015
Externally published	Yes
Event	12th International Symposium on Mathematical Morphology, ISMM 2015 - Reykjavik, Iceland Duration: 27 May 2015 → 29 May 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9082
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Symposium on Mathematical Morphology, ISMM 2015
Country/Territory	Iceland
City	Reykjavik
Period	27/05/15 → 29/05/15

Keywords

Berkeley segmentation database
Stochastic watershed
Unsupervised image segmentation

Access to Document

10.1007/978-3-319-18720-4_36

Cite this

Franchi, G., & Angulo, J. (2015). Bagging stochastic watershed on natural color image segmentation. In L. Najman, H. Talbot, J. A. Benediktsson, & J. Chanussot (Eds.), Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings (pp. 422-433). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9082). Springer Verlag. https://doi.org/10.1007/978-3-319-18720-4_36

Franchi, Gianni ; Angulo, Jesús. / Bagging stochastic watershed on natural color image segmentation. Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings. editor / Laurent Najman ; Hugues Talbot ; Jon Atli Benediktsson ; Jocelyn Chanussot. Springer Verlag, 2015. pp. 422-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochastic approach to improve the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.",

keywords = "Berkeley segmentation database, Stochastic watershed, Unsupervised image segmentation",

author = "Gianni Franchi and Jes{\'u}s Angulo",

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doi = "10.1007/978-3-319-18720-4\_36",

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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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Franchi, G & Angulo, J 2015, Bagging stochastic watershed on natural color image segmentation. in L Najman, H Talbot, JA Benediktsson & J Chanussot (eds), Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9082, Springer Verlag, pp. 422-433, 12th International Symposium on Mathematical Morphology, ISMM 2015, Reykjavik, Iceland, 27/05/15. https://doi.org/10.1007/978-3-319-18720-4_36

Bagging stochastic watershed on natural color image segmentation. / Franchi, Gianni; Angulo, Jesús.
Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings. ed. / Laurent Najman; Hugues Talbot; Jon Atli Benediktsson; Jocelyn Chanussot. Springer Verlag, 2015. p. 422-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9082).

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

TY - GEN

T1 - Bagging stochastic watershed on natural color image segmentation

AU - Franchi, Gianni

AU - Angulo, Jesús

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochastic approach to improve the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.

AB - The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochastic approach to improve the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.

KW - Berkeley segmentation database

KW - Stochastic watershed

KW - Unsupervised image segmentation

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U2 - 10.1007/978-3-319-18720-4_36

DO - 10.1007/978-3-319-18720-4_36

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AN - SCOPUS:84945972016

SN - 9783319187198

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings

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Franchi G, Angulo J. Bagging stochastic watershed on natural color image segmentation. In Najman L, Talbot H, Benediktsson JA, Chanussot J, editors, Mathematical Morphology and its Applications to Signal and Image Processing - 12th International Symposium, ISMM 2015, Proceedings. Springer Verlag. 2015. p. 422-433. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-18720-4_36

Bagging stochastic watershed on natural color image segmentation

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