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Convolution computations can be simplified by utilizing their inherent properties.
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The important convolution properties include width, area, differentiation, and integration properties.
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Deep Neural Networks for Image-Based Dietary Assessment
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Content-Based Image Retrieval for Traditional Indonesian Woven Fabric Images Using a Modified Convolutional Neural

Silvester Tena1,2, Rudy Hartanto1, Igi Ardiyanto1

  • 1Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia.

Journal of Imaging
|August 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the TenunIkatNet dataset for Indonesian ikat woven fabrics. A modified convolutional neural network (MCNN) achieved high accuracy in image retrieval, aiding artisans and trade.

Keywords:
ikat woven fabricimage retrievalmodified CNNpretrained CNN

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Area of Science:

  • Computer Science
  • Textile Arts
  • Cultural Heritage

Background:

  • Content-based image retrieval (CBIR) systems can support Indonesian traditional woven fabric artisans and trade.
  • Developing effective CBIR systems is challenging due to limited datasets and the need to consider unique fabric characteristics simultaneously.

Purpose of the Study:

  • To create the TenunIkatNet dataset, a specialized collection of Indonesian ikat woven fabric images.
  • To develop and evaluate a modified convolutional neural network (MCNN) for efficient and accurate retrieval of these fabrics.

Main Methods:

  • Collected 4800 images across 120 classes of Indonesian ikat woven fabrics.
  • Captured images under various conditions (perpendicularly, different backgrounds, utilized forms).
  • Employed a modified convolutional neural network (MCNN) for feature extraction and image retrieval.

Main Results:

  • The TenunIkatNet dataset comprises 120 classes and 4800 images.
  • The MCNN model demonstrated superior performance compared to established pretrained CNN models.
  • Achieved high retrieval accuracies: 99.96% (top-5), 99.88% (top-10), 99.50% (top-20), and 97.60% (top-50).

Conclusions:

  • The developed TenunIkatNet dataset and MCNN provide an effective solution for Indonesian ikat woven fabric retrieval.
  • This system can significantly benefit artisans, cultural preservation, and trade promotion efforts.
  • The MCNN's performance highlights its potential for specialized image retrieval tasks.