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Optimization of Artistic Image Segmentation Algorithm Based on Feed Forward Neural Network under Complex Background Environment.

Based on the theory and application, this paper discusses the optimization of art image segmentation algorithm based on FFNN (Feed Forward Neural Network). In this paper, residual units are used in the corresponding stages of encoder and decoder, and feature information of several convolution layers...

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Published in:Journal of Environmental & Public Health pp. 1 - 12
Main Author: Li, Yibiao
Format: Journal Article
Published: Wiley-Blackwell 9/13/2022
Online Access:View this record in EBSCOhost
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        16879805
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      jtl: Journal of Environmental & Public Health
      issn: 16879805
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    pubinfo:
      dt: 9/13/2022
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        159075519
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        NLM36148406
        10.1155/2022/9454344
        NLM36148406
        159075519
      ppf: 1
      ppct: 11
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      tig:
        atl: Optimization of Artistic Image Segmentation Algorithm Based on Feed Forward Neural Network under Complex Background Environment.
      aug:
        au: Li, Yibiao
        affil: School of Arts and Tourism, Lianyungang Technical College, Lianyungang 222000, China
      sug:
        subj:
          Image Processing, Computer Assisted Methods
          Algorithms
          Short Portable Mental Status Questionnaire
      ab: Based on the theory and application, this paper discusses the optimization of art image segmentation algorithm based on FFNN (Feed Forward Neural Network). In this paper, residual units are used in the corresponding stages of encoder and decoder, and feature information of several convolution layers in each convolution stage of encoder is extracted at the same time. And the feature pyramid module is used to extract multiscale features from the feature map of the last convolution stage in the encoder. Finally, pixel by pixel additions combine the previously mentioned feature information into the corresponding layer of the decoder. Additionally, an improved weight adaptive algorithm based on feature preservation is suggested in this paper, which addresses the issue that the conventional image segmentation algorithm is noise-sensitive. The adaptive connection weight mechanism is also introduced. The accuracy and recall rates of this optimization algorithm can both reach 96.574%, according to the results of 50% cross-validation. All the segmentation performance evaluation indexes of this algorithm are higher than the existing main algorithms. Moreover, the algorithm takes a short time, does not need too much manual intervention, and can effectively segment artistic images. The optimization algorithm in this paper has certain reference significance for the related research of artistic image segmentation.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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