SketchSegNet+: An End-to-end Learning of RNN for Multi-Class Sketch Semantic Segmentation

Y. Qi, Z. Tan

Research output: Contribution to journalJournal articleResearchpeer-review

3 Downloads (Pure)
Original languageEnglish
JournalIEEE Access
Volume7
Pages (from-to)102717-102726
Number of pages10
ISSN2169-3536
DOIs
Publication statusPublished - 18 Jul 2019

Cite this

@article{446ad740ac6147789c358663b89d5703,
title = "SketchSegNet+: An End-to-end Learning of RNN for Multi-Class Sketch Semantic Segmentation",
keywords = "Semantics, Image segmentation, Recurrent neural networks, Labeling, Image retrieval, Task analysis, Licenses, Stroke-level Sketch Segmentation, Recurrent Neural Network",
author = "Y. Qi and Z. Tan",
year = "2019",
month = "7",
day = "18",
doi = "10.1109/ACCESS.2019.2929804",
language = "English",
volume = "7",
pages = "102717--102726",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE",

}

SketchSegNet+ : An End-to-end Learning of RNN for Multi-Class Sketch Semantic Segmentation. / Qi, Y.; Tan, Z.

In: IEEE Access, Vol. 7, 18.07.2019, p. 102717-102726.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - SketchSegNet+

T2 - An End-to-end Learning of RNN for Multi-Class Sketch Semantic Segmentation

AU - Qi, Y.

AU - Tan, Z.

PY - 2019/7/18

Y1 - 2019/7/18

KW - Semantics

KW - Image segmentation

KW - Recurrent neural networks

KW - Labeling

KW - Image retrieval

KW - Task analysis

KW - Licenses

KW - Stroke-level Sketch Segmentation

KW - Recurrent Neural Network

U2 - 10.1109/ACCESS.2019.2929804

DO - 10.1109/ACCESS.2019.2929804

M3 - Journal article

VL - 7

SP - 102717

EP - 102726

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -