2D freehand sketch labeling using CNN and CRF

Zhu, Xiang; Xiao, Yi; Zheng, Yujun

doi:10.1007/s11042-019-08158-z

Cited by 16 publications

(5 citation statements)

References 38 publications

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“…It can be used for the calculation of graphics three dimensions and can represent complex geometric structures. Laplacian matrix is an excellent method for processing graph information due to its positive definiteness [21], so Chebyshev convolution based on Laplacian spectral decomposition is used to extract the non-Euclidean correlation of joint points. At the same time, the parameter K is used to distinguish the features of adjacent nodes of different orders.…”

Section: Graph Spatiotemporal Feature Extraction Based Onmentioning

confidence: 99%

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Jin-wen

Shen

2022

Wireless Communications and Mobile Computing

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Action recognition is the basis of intelligent sports training and has a unique role in improving athletes’ sports training ability. Traditional motion recognition cannot accurately identify changes in human skeletal motion, and the generalization ability is weak, which cannot meet the needs of modern sports. This paper is aimed at studying a new method of human motion recognition and better realizing the intelligent development of the Institute of Physical Education. This paper proposes an improved recognition mode of spatiotemporal graph convolutional network, builds a human skeleton feature recognition model, learns the changes of human skeleton motion, and masters the laws of human skeleton motion changes. This paper comprehensively compares the differences of action recognition of several different algorithms and proves the superiority of this method. Afterwards, a comprehensive comparison of 2D and 3D information data and a comparison of whole body and local action recognition methods were conducted to investigate the experience of athletes and trainers. The experimental results of this paper show that the spatiotemporal graph convolutional network can better realize the recognition of motion features, improve the sports training ability of athletes by 20%, and promote the development of sports.

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Section: Graph Spatiotemporal Feature Extraction Based Onmentioning

confidence: 99%

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Jin-wen

Shen

2022

Wireless Communications and Mobile Computing

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“…While some results could be achieved, these methods highly rely on specific input format and are time consuming. Following the flourishing of deep learning, various neural network architectures are used for SSS, including CNN-based methods [13][14][15][16], and RNN-based methods [17][18][19][20][21]. CNN-based models treat SSS as an image segmentation task and pay more attention to the edge and outline features.…”

Section: Sketch Semantic Segmentationmentioning

confidence: 99%

Stroke-based semantic segmentation for scene-level free-hand sketches

Zhang

Deng

et al. 2022

Vis Comput

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Sketching is a simple and efficient way for humans to express their perceptions of the world. Sketch semantic segmentation plays a key role in sketch understanding, and is widely used in sketch recognition, sketch-based image retrieval or editing. Due to modality difference between images and sketches, existing image segmentation methods may not perform best, which overlook the sparse nature and stroke-based representation in sketches. The existing sketch semantic segmentation methods are mainly designed for single instance sketches. In this paper, we present a new Stroke-based Sequential-Spatial Neural Network (S 3 NN) for scene-level free-hand sketch semantic segmentation, which leverages a bidirectional LSTM and graph convolutional network to capture the sequential and spatial features of sketches. In order to address the data lacking issue, we propose the first Scene-level Free-hand Sketch Dataset (SFSD). SFSD is composed of 12K sketch-photo pairs over 40 object categories, where the sketches were completely hand-drawn and each contains 7 objects on average. We conduct comparative and ablative experiments on SFSD to evaluate the effectiveness of our method. The experimental results demonstrate that our method outperforms state-of-the-art (SOTA) methods. The code, models and dataset will be made public after acceptance.

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“…[28] introduces a two-level Convolutional Neural Network (CNN) to parse an image of sketched objects into semantic regions. [17,41] take sketches as sketch images, and adapt existing CNNbased semantic image segmentation methods for sketch segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, deep learning methods greatly improve both the segmentation accuracy and the efficiency. They can be roughly grouped into two classes: image-based methods [28,17,41] and sequencebased methods [16,26,36]. Image-based methods treat the task as a semantic image segmentation problem and use convolutional neural networks to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

Yang

Zhuang

et al. 2020

Preprint

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We introduce SketchGCN, a graph convolutional neural network for semantic segmentation and labeling of freehand sketches. We treat an input sketch as a 2D point set, and encode the stroke structure information into graph node/edge representations. To predict the per-point labels, our SketchGCN uses graph convolution and a globallocal branching network architecture to extract both intrastroke and inter-stroke features. SketchGCN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.4% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequencebased methods.

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2D freehand sketch labeling using CNN and CRF

Cited by 16 publications

References 38 publications

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Recognition of Hitting Action in Cyclic Anaerobic Volleyball by Acute Cooling Based on Improved Spatiotemporal Graph Convolutional Network

Stroke-based semantic segmentation for scene-level free-hand sketches

SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

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