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

    2 Citations (Scopus)

    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
    Country/TerritoryKorea, Republic of
    CityJeju Island
    Period20/10/2120/10/23

    Bibliographical note

    Publisher Copyright:
    © 2020 IEEE.

    Keywords

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

    ASJC Scopus subject areas

    • Information Systems
    • Computer Networks and Communications

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