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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lingyan Ran Yanning Zhang Gang Hua |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Comput. Sci., Northwestern Polytech. Univ., Xian, China (Lingyan Ran; Yanning Zhang) || Dept. of Comput. Sci., Stevens Inst. of Technol., Hoboken, NJ, USA (Gang Hua) |
| Abstract | Semantic segmentation has long been a hot topic, most methods are the region based method, which lost connection information to their neighbors. In this paper we propose to encode context information into convolutional networks on this semantic labeling task. Firstly, we propose the nonlocal convolution kernel, which extracts feature from larger neighbor regions without introducing more parameters. Then we build up a context aware module, which takes both local patch and nonlocal neighbor information into account. At last we embed the module into convolutional networks and tested the improvement on benchmark datasets. |
| Starting Page | 4669 |
| Ending Page | 4673 |
| File Size | 1329142 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479983391 |
| DOI | 10.1109/ICIP.2015.7351692 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-09-27 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Context-aware services Feature extraction Context Training Semantics Image segmentation context aware module Semantic segmentation sparse kernel |
| Content Type | Text |
| Resource Type | Article |
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