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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| Summary: | 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. |
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| ISSN: | 1687-5680 1687-5699 |