Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks
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 consi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2018/8207201 |
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| _version_ | 1850177685888696320 |
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| author | Xiaoli Zhao Guozhong Wang Jiaqi Zhang Xiang Zhang |
| author_facet | Xiaoli Zhao Guozhong Wang Jiaqi Zhang Xiang Zhang |
| author_sort | Xiaoli Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c39e1f67b2034cb3a2d597d0774c6f49 |
| institution | OA Journals |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-c39e1f67b2034cb3a2d597d0774c6f492025-08-20T02:18:55ZengWileyAdvances in Multimedia1687-56801687-56992018-01-01201810.1155/2018/82072018207201Scene Understanding Based on High-Order Potentials and Generative Adversarial NetworksXiaoli Zhao0Guozhong Wang1Jiaqi Zhang2Xiang Zhang3School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaScene 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.http://dx.doi.org/10.1155/2018/8207201 |
| spellingShingle | Xiaoli Zhao Guozhong Wang Jiaqi Zhang Xiang Zhang Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks Advances in Multimedia |
| title | Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks |
| title_full | Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks |
| title_fullStr | Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks |
| title_full_unstemmed | Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks |
| title_short | Scene Understanding Based on High-Order Potentials and Generative Adversarial Networks |
| title_sort | scene understanding based on high order potentials and generative adversarial networks |
| url | http://dx.doi.org/10.1155/2018/8207201 |
| work_keys_str_mv | AT xiaolizhao sceneunderstandingbasedonhighorderpotentialsandgenerativeadversarialnetworks AT guozhongwang sceneunderstandingbasedonhighorderpotentialsandgenerativeadversarialnetworks AT jiaqizhang sceneunderstandingbasedonhighorderpotentialsandgenerativeadversarialnetworks AT xiangzhang sceneunderstandingbasedonhighorderpotentialsandgenerativeadversarialnetworks |