SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks

Junkun Jiang, Ruomei Wang, Shujin Lin, Fei Wang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different drawing styles and skills; (ii) distorting lines drawn on the touchpad make sketches more difficult to be recognized; (iii) the high-performance image segmentation via deep learning technologies needs enormous annotated sketch datasets during the training stage. In this paper, we propose a Sketch-target deep FCN Segmentation Network(SFSegNet) for automatic free-hand sketch segmentation, labeling each sketch in a single object with multiple parts. SFSegNet has an end-to-end network process between the input sketches and the segmentation results, composed of 2 parts: (i) a modified deep Fully Convolutional Network(FCN) using a reweighting strategy to ignore background pixels and classify which part each pixel belongs to; (ii) affine transform encoders that attempt to canonicalize the shaking strokes. We train our network with the dataset that consists of 10,000 annotated sketches, to find an extensively applicable model to segment stokes semantically in one ground truth. Extensive experiments are carried out and segmentation results show that our method outperforms other state-of-the-art networks.
Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)9781728119854
ISBN (Print)9781728119861
DOIs
Publication statusPublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019
https://ieeexplore.ieee.org/xpl/conhome/8840768/proceeding

Publication series

NameInternational Joint Conference on Neural Networks (IJCNN)
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/TerritoryHungary
CityBudapest
Period14/07/1919/07/19
Internet address

User-Defined Keywords

  • sketch segmentation
  • object segmentation
  • FCN
  • deep learning

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