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Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part I

Research Article

Research on Image Recognition Algorithm Based on Depth Level Feature

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  • @INPROCEEDINGS{10.1007/978-3-030-51100-5_37,
        author={Chan Zhang and Jia Luo},
        title={Research on Image Recognition Algorithm Based on Depth Level Feature},
        proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part I},
        proceedings_a={ICMTEL},
        year={2020},
        month={7},
        keywords={Depth level features Image recognition Algorithm research},
        doi={10.1007/978-3-030-51100-5_37}
    }
    
  • Chan Zhang
    Jia Luo
    Year: 2020
    Research on Image Recognition Algorithm Based on Depth Level Feature
    ICMTEL
    Springer
    DOI: 10.1007/978-3-030-51100-5_37
Chan Zhang1,*, Jia Luo1
  • 1: School of Information Technology, Guangdong Industry Polytechnic
*Contact email: zhangchan332@163.com

Abstract

In order to solve the problem that the traditional image recognition algorithm can not guarantee a relatively stable recognition accuracy and poor robustness under a variety of interferences, the image recognition algorithm based on depth level features is studied. After preprocessing, such as filtering and enhancing, the image to be recognized is segmented. The segmented image is input into convolution neural network, and the feature of depth level is extracted from the neural network. The feature points of the extracted deep level features are matched to realize image recognition, and the algorithm of image recognition based on the deep level features is designed. Compared with the traditional image recognition algorithm, the designed image recognition algorithm can ensure a more stable recognition accuracy and has better robustness.

Keywords
Depth level features Image recognition Algorithm research
Published
2020-07-19
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-51100-5_37
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