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Digital Image Feature Recognition Method of Mobile Platform Based on Machine Learning

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Abstract

To solve the problem of low recognition accuracy and long recognition time in conventional methods for digital moving image feature recognition, a method for digital image feature recognition on mobile platforms based on machine learning is proposed. The support vector machine method is used to cluster the dataset, and then the weight reconstruction method is used to reduce the dimension of digital moving image data. The reduced dimension of the digital moving image is embedded in the coordinates to obtain the reduced dimension of the image data information and complete the feature recognition of the digital moving image. The simulation results show that the recognition accuracy of this method in recognizing features of moving digital images is basically stable at more than 95% and the running time is 0.42s. The recognition effect of the proposed method is good, the recognition efficiency is high, and it has some practical application value.

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Authors and Affiliations

  1. Wesleyan College of Manila, Cavite State University, Metro Manila, Philippines

    Min-Qing Yang

  2. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

Authors
  1. Min-Qing Yang
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  2. Jerry Chun-Wei Lin
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Contributions

Minqing Yang provided the algorithm and experimental results, wrote the manuscript, Jerry Chun-Wei Lin revised the paper, supervised and analyzed the experiment.

Corresponding author

Correspondence to Jerry Chun-Wei Lin.

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Yang, MQ., Lin, J.CW. Digital Image Feature Recognition Method of Mobile Platform Based on Machine Learning. Mobile Netw Appl 27, 2506–2514 (2022). https://doi.org/10.1007/s11036-022-02069-4

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