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Hand-Crafted and Learned Feature Aggregation for Visual Marble Tiles Screening.

George K Sidiropoulos1, Athanasios G Ouzounis1, George A Papakostas1

  • 1MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece.

Journal of Imaging
|July 25, 2022
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Summary
This summary is machine-generated.

This study automates marble tile classification using combined texture descriptors. Aggregating VGG16 and SILTP achieved 0.9944 AUC, improving objective quality assessment and marketing.

Keywords:
CNNdeep learningfeature fusionmachine learningmarble tile sortingtexture description

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Area of Science:

  • Materials Science
  • Computer Vision
  • Geology

Background:

  • Manual classification of marble tiles is subjective and leads to marketing issues.
  • Matching tile textures are crucial for natural ornamental rock sales.
  • Automating classification is needed for objective quality assessment.

Purpose of the Study:

  • To automate the classification of marble tiles.
  • To evaluate hand-crafted and Convolutional Neural Network (CNN) texture descriptors.
  • To create aggregated descriptors for objective classification.

Main Methods:

  • Evaluated 24 hand-crafted and 20 CNN texture descriptors.
  • Combined one hand-crafted and one CNN descriptor at a time.
  • Developed and tested an automatic screening machine with aggregated descriptors.

Main Results:

  • The aggregation of VGG16 (CNN) and SILTP (hand-crafted) yielded the best performance.
  • Achieved an Area Under the Curve (AUC) score of 0.9944.
  • The developed model was integrated into an automated screening system.

Conclusions:

  • Automated classification using aggregated descriptors ensures objective quality assessment.
  • The VGG16 and SILTP combination offers a highly accurate method for marble tile classification.
  • Publicly released dataset facilitates further research in ornamental marble analysis.