Structured Support Vector Machine Learning of Conditional Random Fields

被引：0

作者：

Rangkuti, Rizki Perdana ^{[1
]}

Mantau, Aprinaldi Jasa ^{[1
]}

Dewanto, Vektor ^{[2
]}

Habibie, Novian ^{[1
]}

Jatmiko, Wisnu ^{[1
]}

机构：

[1] Univ Indonesia, Fac Comp Sci, Jawa Barat, Indonesia

[2] Bogor Agr Univ, Dept Comp Sci, Jawa Barat, Indonesia

来源：

2016 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND INFORMATION SYSTEMS (ICACSIS) | 2016年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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 I-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).

引用

页码：548 / 555

页数：8

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