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Advances in Multimedia
Volume 2018, Article ID 8207201, 8 pages
https://doi.org/10.1155/2018/8207201
Research Article

Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Correspondence should be addressed to Xiaoli Zhao; moc.361@yhwave

Received 31 May 2018; Accepted 19 July 2018; Published 5 August 2018

Academic Editor: Shih-Chia Huang

Copyright © 2018 Xiaoli Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Scene understanding is to predict a class label at each pixel of an image. In this study, we propose a semantic segmentation framework based on classic generative adversarial nets (GAN) to train a fully convolutional semantic segmentation model along with an adversarial network. To improve the consistency of the segmented image, the high-order potentials, instead of unary or pairwise potentials, are adopted. We realize the high-order potentials by substituting adversarial network for CRF model, which can continuously improve the consistency and details of the segmented semantic image until it cannot discriminate the segmented result from the ground truth. A number of experiments are conducted on PASCAL VOC 2012 and Cityscapes datasets, and the quantitative and qualitative assessments have shown the effectiveness of our proposed approach.