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High Similarity Image Recognition and Classification Algorithm Based on Convolutional Neural Network.

Zhizhe Liu1, Luo Sun2, Qian Zhang1

  • 1School of Art and Design, Wuhan University of Technology, Wuhan 430070, China.

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Summary
This summary is machine-generated.

This study introduces a novel convolutional neural network algorithm for high similarity image recognition. The method achieves over 90% accuracy in classifying challenging datasets like gems and determining apple origins.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Advancements in computing power support sophisticated neural network technologies.
  • Convolutional neural networks (CNNs) excel in computer vision tasks like image recognition.
  • High similarity image classification presents a significant challenge in specific domains.

Purpose of the Study:

  • To propose a CNN-fused algorithm for high similarity image recognition and classification.
  • To enhance the accuracy of image classification in specialized fields.
  • To develop a robust method for identifying subtle differences in images.

Main Methods:

  • Image texture and energy features are extracted and analyzed.
  • Images are decomposed into subimages based on texture differences.
  • A 5-layer CNN with alternating convolution and pooling layers is employed.
  • Feature fusion is achieved using convolution kernels of varying sizes.
  • Network parameters are optimized through increased training data and iterations.

Main Results:

  • The algorithm demonstrates high characterization capabilities for image recognition.
  • Optimal texture and feature extraction parameters were determined.
  • The CNN model achieved high classification accuracy on gem and apple datasets.
  • Average identification accuracy exceeded 90% in experimental evaluations.

Conclusions:

  • The proposed CNN-fused algorithm effectively addresses high similarity image recognition challenges.
  • The method offers a robust solution for classifying visually similar objects.
  • The approach shows significant potential for applications in gem identification and agricultural product traceability.