Contextual Image Segmentation based on AdaBoost and Markov Random Fields

Ryuei Nishii

Contextual Image Segmentation based on AdaBoost and Markov Random Fields

Ryuei Nishii

Research output: Contribution to conference › Paper › peer-review

Abstract

AdaBoost, one of machine learning algorithms, is employed for classification of land-cover categories of geostatistical data. We assume that the posterior probability is given by the odds ratio due to loss functions. Further, landcover categories are assumed to follow Markov random fields (MRF). Then, we derive a classifier by combining two posteriors based on AdaBoost and MRF through the iterative conditional modes. Our procedure is applied to benchmark data sets provided by IEEE GRSS Data Fusion Committee and shows an excellent performance.

Original language	English
Pages	3507-3509
Number of pages	3
Publication status	Published - 2003
Externally published	Yes
Event	2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France Duration: Jul 21 2003 → Jul 25 2003

Other

Other	2003 IGARSS: Learning From Earth's Shapes and Colours
Country/Territory	France
City	Toulouse
Period	7/21/03 → 7/25/03

All Science Journal Classification (ASJC) codes

Computer Science Applications
General Earth and Planetary Sciences

Cite this

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title = "Contextual Image Segmentation based on AdaBoost and Markov Random Fields",

abstract = "AdaBoost, one of machine learning algorithms, is employed for classification of land-cover categories of geostatistical data. We assume that the posterior probability is given by the odds ratio due to loss functions. Further, landcover categories are assumed to follow Markov random fields (MRF). Then, we derive a classifier by combining two posteriors based on AdaBoost and MRF through the iterative conditional modes. Our procedure is applied to benchmark data sets provided by IEEE GRSS Data Fusion Committee and shows an excellent performance.",

author = "Ryuei Nishii",

year = "2003",

language = "English",

pages = "3507--3509",

note = "2003 IGARSS: Learning From Earth's Shapes and Colours ; Conference date: 21-07-2003 Through 25-07-2003",

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T1 - Contextual Image Segmentation based on AdaBoost and Markov Random Fields

AU - Nishii, Ryuei

PY - 2003

Y1 - 2003

N2 - AdaBoost, one of machine learning algorithms, is employed for classification of land-cover categories of geostatistical data. We assume that the posterior probability is given by the odds ratio due to loss functions. Further, landcover categories are assumed to follow Markov random fields (MRF). Then, we derive a classifier by combining two posteriors based on AdaBoost and MRF through the iterative conditional modes. Our procedure is applied to benchmark data sets provided by IEEE GRSS Data Fusion Committee and shows an excellent performance.

AB - AdaBoost, one of machine learning algorithms, is employed for classification of land-cover categories of geostatistical data. We assume that the posterior probability is given by the odds ratio due to loss functions. Further, landcover categories are assumed to follow Markov random fields (MRF). Then, we derive a classifier by combining two posteriors based on AdaBoost and MRF through the iterative conditional modes. Our procedure is applied to benchmark data sets provided by IEEE GRSS Data Fusion Committee and shows an excellent performance.

UR - https://www.scopus.com/pages/publications/0242457294

UR - https://www.scopus.com/pages/publications/0242457294#tab=citedBy

M3 - Paper

AN - SCOPUS:0242457294

SP - 3507

EP - 3509

T2 - 2003 IGARSS: Learning From Earth's Shapes and Colours

Y2 - 21 July 2003 through 25 July 2003

ER -

Contextual Image Segmentation based on AdaBoost and Markov Random Fields

Abstract

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All Science Journal Classification (ASJC) codes

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