Structured support vector machine learning of conditional random fields

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (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 languageEnglish
Title of host publication2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages548-555
Number of pages8
ISBN (Electronic)9781509046294
DOIs
Publication statusPublished - 6 Mar 2017
Event8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 - Malang, Indonesia
Duration: 15 Oct 201616 Oct 2016

Publication series

Name2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016

Conference

Conference8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016
CountryIndonesia
CityMalang
Period15/10/1616/10/16

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