@inproceedings{768e027a9c794101b6124fd67a211778,
title = "Exploiting deep convolutional network and patch-level CRFs for indoor semantic segmentation",
abstract = "Recent advances in semantic segmentation have mostly been achieved by utilizing deep convolutional neural networks (CNNs). In this paper, a novel indoor semantic segmentation method is proposed by integrating CNN and patch-level Conditional random fields (CRF). Multi-scale images are sent to CNN to capture objects of different sizes as well as to extract features at multiple scales. Patch-level CRF is constructed to further refine the object boundaries localization accuracy. Extensive experiments on the publicly available NYU V2 database demonstrate that the proposed method could obtain state-of-the-art accuracy in terms of four evaluation metrics.",
author = "Xingming Wu and Mengnan Du and Weihai Chen and Zhengguo Li",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 ; Conference date: 05-06-2016 Through 07-06-2016",
year = "2016",
month = oct,
day = "19",
doi = "10.1109/ICIEA.2016.7603568",
language = "English (US)",
series = "Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "150--155",
booktitle = "Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016",
address = "United States",
}