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

Dongkwon Jin; Chang Su Kim

doi:10.1109/ICTC49870.2020.9289236

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

Dongkwon Jin
, Chang Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	ICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publication	Data, Network, and AI in the Age of Untact
Publisher	IEEE Computer Society
Pages	391-393
Number of pages	3
ISBN (Electronic)	9781728167589
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289236
Publication status	Published - 2020 Oct 21
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 2020 Oct 21 → 2020 Oct 23

Publication series

Name	International Conference on ICT Convergence
Volume	2020-October
ISSN (Print)	2162-1233
ISSN (Electronic)	2162-1241

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	20/10/21 → 20/10/23

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

Access to Document

10.1109/ICTC49870.2020.9289236

Cite this

Jin, D., & Kim, C. S. (2020). Score Prediction Network and Graph-based Selection for Semantic Line Detection. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 391-393). Article 9289236 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289236

Score Prediction Network and Graph-based Selection for Semantic Line Detection. / Jin, Dongkwon; Kim, Chang Su.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 391-393 9289236 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Jin, D & Kim, CS 2020, Score Prediction Network and Graph-based Selection for Semantic Line Detection. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289236, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 391-393, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 20/10/21. https://doi.org/10.1109/ICTC49870.2020.9289236

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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.",

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AB - 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.

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this