Score Prediction Network and Graph-based Selection for Semantic Line Detection

Dongkwon Jin, Chang Su Kim

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

Abstract

In this paper, we propose a novel semantic line detection algorithm. For an input image, we first detect semantic lines using a semantic line detector by classifying candidate lines. Then, we predict scores indicating whether they are harmonized or not between the detected lines. To this end, we develop a score prediction network (SPNet). Finally, we construct a graph consisting of the detected lines and the predicted scores between them and iteratively select the reliable semantic lines. Experimental results demonstrate that the proposed algorithm detects semantic lines accurately.

Original languageEnglish
Title of host publicationICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publicationData, Network, and AI in the Age of Untact
PublisherIEEE Computer Society
Pages391-393
Number of pages3
ISBN (Electronic)9781728167589
DOIs
Publication statusPublished - 2020 Oct 21
Event11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of
Duration: 2020 Oct 212020 Oct 23

Publication series

NameInternational Conference on ICT Convergence
Volume2020-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference11th International Conference on Information and Communication Technology Convergence, ICTC 2020
CountryKorea, Republic of
CityJeju Island
Period20/10/2120/10/23

Keywords

  • Semantic lines
  • graph-based selection
  • line detection
  • score prediction

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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