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Transfer learning for image classification using VGG19: Caltech-101 image data set.

Monika Bansal1, Munish Kumar2, Monika Sachdeva3

  • 1SSD Women Institute of Technology, Bathinda, Punjab India.

Journal of Ambient Intelligence and Humanized Computing
|September 22, 2021
PubMed
Summary
This summary is machine-generated.

Combining deep learning features with traditional handcrafted methods significantly boosts image classification accuracy. This hybrid approach, using VGG19 and methods like SIFT, outperforms single-feature extractors for better computer vision results.

Keywords:
K-MeansLPPORBPCASIFTSURF

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Image classification is a key area in computer vision, with deep learning showing strong performance.
  • However, deep learning methods sometimes fail to extract all crucial image information, limiting classification accuracy.

Purpose of the Study:

  • To enhance image classification performance by integrating deep features from VGG19 with handcrafted features.
  • To evaluate the effectiveness of combining diverse feature extraction techniques for improved accuracy.

Main Methods:

  • Extracted deep features using VGG19 and handcrafted features (SIFT, SURF, ORB, Shi-Tomasi).
  • Classified features using Gaussian Naïve Bayes, Decision Tree, Random Forest, and XGBoost on the Caltech-101 dataset.

Main Results:

  • The Random Forest classifier achieved 93.73% accuracy when using combined deep and handcrafted features.
  • This hybrid approach surpassed other classifiers and previously proposed methods on the Caltech-101 dataset.

Conclusions:

  • Neither deep learning nor handcrafted features alone are sufficient for optimal image classification.
  • A combined strategy leveraging both deep and traditional features offers superior performance in image classification tasks.