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Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems.

Hong-An Li1, Qiaoxue Zheng1, Xin Qi2

  • 1College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.

Computational Intelligence and Neuroscience
|September 3, 2021
PubMed
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This study enhances image style transfer using VGG-19 networks with L1 and perceptual losses. The improved deep learning model effectively preserves image content while achieving better style transfer results.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Image style transfer is crucial for big data applications, utilizing neural networks for image data mining.
  • Deep learning methods for style transfer often result in the loss of original image content information.

Purpose of the Study:

  • To address content loss in deep learning-based image style transfer.
  • To improve the model's perceptual ability and maintain image content during style transfer.

Main Methods:

  • Incorporated L1 loss into the VGG-19 network to minimize discrepancies between image style and content.
  • Introduced perceptual loss to compute feature map semantic information, enhancing model perception.

Main Results:

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  • The proposed method significantly improves style transfer capabilities while preserving image content.
  • Evaluation metrics showed increases in structural similarity (0.323%), cosine similarity (0.094%), and mutual information (3.591%).

Conclusions:

  • The enhanced model provides superior stylization that better aligns with user expectations.
  • The integration of L1 and perceptual losses offers a robust solution for content-aware image style transfer.