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).