ct 19(19): e5

Research Article

LW-Sketch-Net on Mobile Phones

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  • @ARTICLE{10.4108/eai.26-4-2019.162611,
        author={Ni Kong and Hong Hou and Zhe Bai},
        title={LW-Sketch-Net on Mobile Phones},
        journal={EAI Endorsed Transactions on Creative Technologies},
        volume={6},
        number={19},
        publisher={EAI},
        journal_a={CT},
        year={2019},
        month={4},
        keywords={Sketch Recognition, Lightweight Networks, Depthwise Separable Convolution},
        doi={10.4108/eai.26-4-2019.162611}
    }
    
  • Ni Kong
    Hong Hou
    Zhe Bai
    Year: 2019
    LW-Sketch-Net on Mobile Phones
    CT
    EAI
    DOI: 10.4108/eai.26-4-2019.162611
Ni Kong1,*, Hong Hou1, Zhe Bai1
  • 1: School of Information Science and Technology, Northwest University, Xi’an, China
*Contact email: kongni929@163.com

Abstract

With the popularity of smart terminals, people tend to draw simple sketches to express emotions and ideas in communication, which means the era of reading pictures is coming. Therefore, in the field of sketch recognition, the application of deep network models in mobile devices is an irreversible trend. However, using deep learning method, most existing works with good performance has a large number of parameters. In order to further improve recognition speed and ensure the accuracy, we propose a lightweight neural network architecture to recognize sketch object. Specifically, we apply depthwise separable convolution into the network to reduce parameters and adjust the network effectively for the sparsity of sketch. Outperforming the state-of-the-art approaches, we achieves 85.8% and 84.1% on TU-Berlin and QuickDraw benchmarks respectively. Furthermore, the number of parameters is reduced to a large extent, which are 5% and 20% of the amount of Sketch-A-Net and MobileNets. We also develop a sketch recognition application on mobile phones to demonstrate the proposed scheme.