Abstract
In this paper we present a relational analysis and verification of multi-labeled images as a result fo semantic segmentation. In semantic segmentation the result is a set of labeled regions but rarely this result is subject to verification that would allow to determine induce the reliability of this segmentation. We show that using shape measure, co-occurrence statistics and specially crafted weighted function we can estimate the correctness of a semantic segmentation up to 92%.
| Original language | English |
|---|---|
| Title of host publication | IDT 2016 - Proceedings of the International Conference on Information and Digital Technologies 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 173-179 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781467388603 |
| DOIs | |
| Publication status | Published - Aug 31 2016 |
| Event | 2016 International Conference on Information and Digital Technologies, IDT 2016 - Rzeszow, Poland Duration: Jul 5 2016 → Jul 7 2016 |
Other
| Other | 2016 International Conference on Information and Digital Technologies, IDT 2016 |
|---|---|
| Country | Poland |
| City | Rzeszow |
| Period | 7/5/16 → 7/7/16 |
Fingerprint
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Signal Processing
Cite this
High-level verification of multi-object segmentation. / Lukac, Martin; Zhurtanov, Almas; Ospanova, Aizhan.
IDT 2016 - Proceedings of the International Conference on Information and Digital Technologies 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 173-179 7557169.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - High-level verification of multi-object segmentation
AU - Lukac, Martin
AU - Zhurtanov, Almas
AU - Ospanova, Aizhan
PY - 2016/8/31
Y1 - 2016/8/31
N2 - In this paper we present a relational analysis and verification of multi-labeled images as a result fo semantic segmentation. In semantic segmentation the result is a set of labeled regions but rarely this result is subject to verification that would allow to determine induce the reliability of this segmentation. We show that using shape measure, co-occurrence statistics and specially crafted weighted function we can estimate the correctness of a semantic segmentation up to 92%.
AB - In this paper we present a relational analysis and verification of multi-labeled images as a result fo semantic segmentation. In semantic segmentation the result is a set of labeled regions but rarely this result is subject to verification that would allow to determine induce the reliability of this segmentation. We show that using shape measure, co-occurrence statistics and specially crafted weighted function we can estimate the correctness of a semantic segmentation up to 92%.
UR - http://www.scopus.com/inward/record.url?scp=84988882851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988882851&partnerID=8YFLogxK
U2 - 10.1109/DT.2016.7557169
DO - 10.1109/DT.2016.7557169
M3 - Conference contribution
AN - SCOPUS:84988882851
SP - 173
EP - 179
BT - IDT 2016 - Proceedings of the International Conference on Information and Digital Technologies 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -