Semantic Line Detection and Its Applications

Jun Tae Lee, Han Ul Kim, Chul Lee, Chang-Su Kim

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

5 Citations (Scopus)

Abstract

Semantic lines characterize the layout of an image. Despite their importance in image analysis and scene understanding, there is no reliable research for semantic line detection. In this paper, we propose a semantic line detector using a convolutional neural network with multi-task learning, by regarding the line detection as a combination of classification and regression tasks. We use convolution and max-pooling layers to obtain multi-scale feature maps for an input image. Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps. Next, we feed the feature vector into the parallel classification and regression layers. The classification layer decides whether the line candidate is semant ic or not. In case of a semantic line, the regression layer determines the offset for refining the line location. Experimental results show that the proposed detector extracts semantic lines accurately and reliably. Moreover, we demonstrate that the proposed detector can be used successfully in three applications: horizon estimation, composition enhancement, and image simplification.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3249-3257
Number of pages9
Volume2017-October
ISBN (Electronic)9781538610329
DOIs
Publication statusPublished - 2017 Dec 22
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 2017 Oct 222017 Oct 29

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period17/10/2217/10/29

Fingerprint

Semantics
Detectors
Convolution
Image analysis
Refining
Neural networks
Chemical analysis

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Lee, J. T., Kim, H. U., Lee, C., & Kim, C-S. (2017). Semantic Line Detection and Its Applications. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (Vol. 2017-October, pp. 3249-3257). [8237612] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.350

Semantic Line Detection and Its Applications. / Lee, Jun Tae; Kim, Han Ul; Lee, Chul; Kim, Chang-Su.

Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2017. p. 3249-3257 8237612.

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

Lee, JT, Kim, HU, Lee, C & Kim, C-S 2017, Semantic Line Detection and Its Applications. in Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. vol. 2017-October, 8237612, Institute of Electrical and Electronics Engineers Inc., pp. 3249-3257, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 17/10/22. https://doi.org/10.1109/ICCV.2017.350
Lee JT, Kim HU, Lee C, Kim C-S. Semantic Line Detection and Its Applications. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Vol. 2017-October. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3249-3257. 8237612 https://doi.org/10.1109/ICCV.2017.350
Lee, Jun Tae ; Kim, Han Ul ; Lee, Chul ; Kim, Chang-Su. / Semantic Line Detection and Its Applications. Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Vol. 2017-October Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3249-3257
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