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Related Experiment Video

Updated: May 21, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Convolutional Fine-Tuned Threshold Adaboost approach for effectual content-based image retrieval.

Robert Cep1, Muniyandy Elangovan2,3, Janjhyam Venkata Naga Ramesh4,5

  • 1Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800, Ostrava, Czech Republic.

Scientific Reports
|March 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Convolutional Fine-Tuned Threshold Adaboost (CFTAB) method to improve content-based image retrieval (CBIR) accuracy. CFTAB integrates deep and machine learning for superior image search performance.

Keywords:
Content-based image retrieval (CBIR)Convolutional Fine-Tuned Threshold Adaboost (CFTAB)Deep and machine learning (DL, ML)High-level informationVGG-16

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Content-based image retrieval (CBIR) is crucial for managing large visual datasets across industries like e-commerce and healthcare.
  • Traditional CBIR methods struggle to extract high-level semantic information, leading to suboptimal retrieval results.
  • The need for enhanced image search accuracy and efficiency is a significant challenge in multimedia data management.

Purpose of the Study:

  • To introduce a novel Convolutional Fine-Tuned Threshold Adaboost (CFTAB) approach for improving content-based image retrieval (CBIR) performance.
  • To address the limitations of traditional CBIR techniques in extracting relevant high-level image features.
  • To enhance the accuracy and efficiency of image search through a hybrid deep learning and machine learning model.

Main Methods:

  • Image data pre-processing using Adaptive Histogram Equalization (AHE).
  • Feature extraction from localized image data utilizing the VGG16 model.
  • Introduction of a novel Convolutional Fine-Tuned Threshold Adaboost (CFTAB) approach integrating deep learning and machine learning.
  • Incorporation of an improved Adaboost (AB) algorithm with dynamic threshold adjustment for classifier optimization.

Main Results:

  • The proposed CFTAB approach demonstrates enhanced performance in content-based image retrieval.
  • Integration of deep learning (VGG16) and machine learning (CFTAB) significantly improves image search quality.
  • Dynamic threshold adjustment in the AB algorithm optimizes classifier training and retrieval outcomes.
  • The CFTAB method effectively extracts high-level features for more relevant image retrieval.

Conclusions:

  • The novel CFTAB approach offers a significant advancement in content-based image retrieval (CBIR) systems.
  • Hybrid deep and machine learning models provide superior performance for complex image search tasks.
  • Dynamic thresholding in machine learning classifiers is effective for optimizing retrieval accuracy.