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sis 22(36): e8

Research Article

DSSESKN: A depthwise separable squeeze-and-excitation selective kernel network for art image classification

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  • @ARTICLE{10.4108/eai.26-11-2021.172304,
        author={Shaojie Zhang},
        title={DSSESKN: A depthwise separable squeeze-and-excitation selective kernel network for art image classification},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={9},
        number={36},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={art image classification, depthwise separable, squeeze-and-excitation, selective kernel network, feature map},
        doi={10.4108/eai.26-11-2021.172304}
    }
    
  • Shaojie Zhang
    Year: 2021
    DSSESKN: A depthwise separable squeeze-and-excitation selective kernel network for art image classification
    SIS
    EAI
    DOI: 10.4108/eai.26-11-2021.172304
Shaojie Zhang1,*
  • 1: School of Art and Design, Zhengzhou University of Industrial Technology, Zhengzhou 450000 China
*Contact email: 352720214@qq.com

Abstract

Image classification is one of the key technologies of content-based image retrieval, and it is also the focus and hotspot of image content analysis research in recent years. Through the image processing and analysis technology to automatically analyze the image content to complete the management and retrieval of images, this process is the main content for image classification. Faced with massive digital Chinese art works, how to achieve effective management and retrieval for them has become an urgent problem to be solved. Traditional image retrieval technology is mainly based on image annotation, which has many problems, such as large workload and not objective enough. In this paper, we propose a depthwise separable squeeze-and-excitation selective kernel network (DSSESKN) for art image classification. SKNet (Slective Kernel Network) is used to adaptively adjust the receptive field to extract the global and detailed features of the image. We use SENet (squeeze-and-excitation network) to enhance the channel features. SKNet and SENet are fused to built the DSSESKN. The convolution kernel on the branch of DSSESKN module is used to extract the global feature and local detail features of the input image. The feature maps on the branches are fused, and the fused feature maps are compressed and activated. The processed feature weights are mapped to the feature maps of different branches and feature fusion is carried out. Art images are classified by deep separable convolution. Finally, we conduct experiments with other state-of-the-art classification methods, the results show that the effectiveness of the DSSESKN obtains the better effect.

Keywords
art image classification, depthwise separable, squeeze-and-excitation, selective kernel network, feature map
Received
2021-11-19
Accepted
2021-11-24
Published
2021-11-26
Publisher
EAI
http://dx.doi.org/10.4108/eai.26-11-2021.172304

Copyright © 2021 Shaojie Zhang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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