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 |
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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 | |
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 |
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Volume | 2020-October |
ISSN (Print) | 2162-1233 |
ISSN (Electronic) | 2162-1241 |
Conference
Conference | 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 |
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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