Multi-column Point-CNN for Sketch Segmentation

Wang, Fei; Lin, Sheng; Li, Hanhui; Wu, Hefeng; Jiang, Junkun; Wang, Ruomei; Luo, Xiangfeng

doi:10.48550/arxiv.1812.11029

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“…As for the dataset, we introduce the instance-level sketch segmentation dataset [25] extended from Huang's benchmark [8], consisting 10,000 annotated sketches, collected by both experts and non-experts depicting objects in ten categories after observing photos or simply imagining. To evaluate the performance of the proposed network architecture, we compare it with state-of-the-art image segmentation method FCN [13], LinkNet-34 [20], U-Net [16] and sketch segmentation method Huang's [8], CRFs [18] on both the introduced dataset and Huang's benchmark.…”

Section: Introductionmentioning

confidence: 99%

SFSegNet: Parse Freehand Sketches using Deep Fully Convolutional Networks

Jiang

Wang

Lin

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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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.

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