Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching

Dongkwon Jin, Jun Tae Lee, Chang Su Kim

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

    9 Citations (Scopus)

    Abstract

    A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experiments demonstrate that the proposed algorithm outperforms the conventional semantic line detector significantly. Moreover, we apply the proposed algorithm to detect two important kinds of semantic lines successfully: dominant parallel lines and reflection symmetry axes. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-DRM.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
    EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages119-135
    Number of pages17
    ISBN (Print)9783030585648
    DOIs
    Publication statusPublished - 2020
    Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
    Duration: 2020 Aug 232020 Aug 28

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12365 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference16th European Conference on Computer Vision, ECCV 2020
    Country/TerritoryUnited Kingdom
    CityGlasgow
    Period20/8/2320/8/28

    Bibliographical note

    Publisher Copyright:
    © 2020, Springer Nature Switzerland AG.

    Keywords

    • Attention
    • Line detection
    • Matching
    • Ranking
    • Semantic lines

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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