Exploiting deep convolutional network and patch-level CRFs for indoor semantic segmentation

Xingming Wu, Mengnan Du, Weihai Chen, Zhengguo Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages150-155
Number of pages6
ISBN (Electronic)9781509026050
DOIs
StatePublished - Oct 19 2016
Externally publishedYes
Event11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 - Hefei, China
Duration: Jun 5 2016Jun 7 2016

Publication series

NameProceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016

Other

Other11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016
Country/TerritoryChina
CityHefei
Period6/5/166/7/16

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Control and Optimization

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