Structured support vector machine learning of conditional random fields

Rizki Perdana Rangkuti; Aprinaldi Jasa Mantau; Vektor Dewanto; Novian Habibie; Wisnu Jatmiko

doi:10.1109/ICACSIS.2016.7872799

Structured support vector machine learning of conditional random fields

Rizki Perdana Rangkuti, Aprinaldi Jasa Mantau, Vektor Dewanto, Novian Habibie, Wisnu Jatmiko

Department of Computer Science

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

2 Citations (Scopus)

Abstract

This research aims to improve the capability of semantic segmentation through data perspective. This research proposed a parameterized Conditional Random Fields model and learns the model by using Structured Support Vector Machine (SSVM). The SSVM utilizes Hamming loss function for optimizing 1-slack Margin Rescaling formulation. The joint feature vector is derived from energy potentials. Variation of image size produces some missing values in the joint feature vector. This research shows that a zero padding can resolve the missing values. The experiment result shows that prediction with parameterized CRF yields 75.867% global accuracy (GA) and 22.1410 % averaged class accuracy (CA).

Original language	English
Title of host publication	2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	548-555
Number of pages	8
ISBN (Electronic)	9781509046294
DOIs	https://doi.org/10.1109/ICACSIS.2016.7872799
Publication status	Published - 6 Mar 2017
Event	8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia Duration: 15 Oct 2016 → 16 Oct 2016

Publication series

Name	2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference	8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
Country/Territory	Indonesia
City	Malang
Period	15/10/16 → 16/10/16

Access to Document

10.1109/ICACSIS.2016.7872799

Cite this

Rangkuti, R. P., Mantau, A. J., Dewanto, V., Habibie, N., & Jatmiko, W. (2017). Structured support vector machine learning of conditional random fields. In 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 (pp. 548-555). Article 7872799 (2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACSIS.2016.7872799

Rangkuti, Rizki Perdana ; Mantau, Aprinaldi Jasa ; Dewanto, Vektor et al. / Structured support vector machine learning of conditional random fields. 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 548-555 (2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016).

@inproceedings{19df242ba3754bf2a0f3e14d73cd7932,

title = "Structured support vector machine learning of conditional random fields",

abstract = "This research aims to improve the capability of semantic segmentation through data perspective. This research proposed a parameterized Conditional Random Fields model and learns the model by using Structured Support Vector Machine (SSVM). The SSVM utilizes Hamming loss function for optimizing 1-slack Margin Rescaling formulation. The joint feature vector is derived from energy potentials. Variation of image size produces some missing values in the joint feature vector. This research shows that a zero padding can resolve the missing values. The experiment result shows that prediction with parameterized CRF yields 75.867% global accuracy (GA) and 22.1410 % averaged class accuracy (CA).",

author = "Rangkuti, {Rizki Perdana} and Mantau, {Aprinaldi Jasa} and Vektor Dewanto and Novian Habibie and Wisnu Jatmiko",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 ; Conference date: 15-10-2016 Through 16-10-2016",

year = "2017",

month = mar,

day = "6",

doi = "10.1109/ICACSIS.2016.7872799",

language = "English",

series = "2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016",

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

pages = "548--555",

booktitle = "2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016",

address = "United States",

}

Rangkuti, RP, Mantau, AJ, Dewanto, V, Habibie, N & Jatmiko, W 2017, Structured support vector machine learning of conditional random fields. in 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016., 7872799, 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016, Institute of Electrical and Electronics Engineers Inc., pp. 548-555, 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016, Malang, Indonesia, 15/10/16. https://doi.org/10.1109/ICACSIS.2016.7872799

Structured support vector machine learning of conditional random fields. / Rangkuti, Rizki Perdana; Mantau, Aprinaldi Jasa; Dewanto, Vektor et al.
2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 548-555 7872799 (2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016).

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

TY - GEN

T1 - Structured support vector machine learning of conditional random fields

AU - Rangkuti, Rizki Perdana

AU - Mantau, Aprinaldi Jasa

AU - Dewanto, Vektor

AU - Habibie, Novian

AU - Jatmiko, Wisnu

PY - 2017/3/6

Y1 - 2017/3/6

N2 - This research aims to improve the capability of semantic segmentation through data perspective. This research proposed a parameterized Conditional Random Fields model and learns the model by using Structured Support Vector Machine (SSVM). The SSVM utilizes Hamming loss function for optimizing 1-slack Margin Rescaling formulation. The joint feature vector is derived from energy potentials. Variation of image size produces some missing values in the joint feature vector. This research shows that a zero padding can resolve the missing values. The experiment result shows that prediction with parameterized CRF yields 75.867% global accuracy (GA) and 22.1410 % averaged class accuracy (CA).

AB - This research aims to improve the capability of semantic segmentation through data perspective. This research proposed a parameterized Conditional Random Fields model and learns the model by using Structured Support Vector Machine (SSVM). The SSVM utilizes Hamming loss function for optimizing 1-slack Margin Rescaling formulation. The joint feature vector is derived from energy potentials. Variation of image size produces some missing values in the joint feature vector. This research shows that a zero padding can resolve the missing values. The experiment result shows that prediction with parameterized CRF yields 75.867% global accuracy (GA) and 22.1410 % averaged class accuracy (CA).

UR - http://www.scopus.com/inward/record.url?scp=85016967245&partnerID=8YFLogxK

U2 - 10.1109/ICACSIS.2016.7872799

DO - 10.1109/ICACSIS.2016.7872799

M3 - Conference contribution

AN - SCOPUS:85016967245

T3 - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

SP - 548

EP - 555

BT - 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Y2 - 15 October 2016 through 16 October 2016

ER -

Rangkuti RP, Mantau AJ, Dewanto V, Habibie N, Jatmiko W. Structured support vector machine learning of conditional random fields. In 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 548-555. 7872799. (2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016). doi: 10.1109/ICACSIS.2016.7872799

Structured support vector machine learning of conditional random fields

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this