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Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with

Manoharan Subramanian1, Velmurugan Lingamuthu1, Chandran Venkatesan2

  • 1Department of Computer Science, School of Informatics and Electrical Engineering, Institute of Technology, Hachalu Hundessa Campus, Ambo University, Ambo, Post Box No.: 19, Ethiopia.

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Summary

This study introduces a new Content-Based Image Retrieval (CBIR) method using color, texture, and shape features. Optimized with Particle Swarm Optimization (PSO) and a random forest classifier, it enhances image retrieval efficiency.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Content-Based Image Retrieval (CBIR) systems are crucial for efficient image searching.
  • Existing CBIR methods often face challenges in accurately retrieving relevant images from large datasets.
  • The need for improved feature extraction and selection techniques in CBIR is evident.

Purpose of the Study:

  • To develop an enhanced Content-Based Image Retrieval (CBIR) system.
  • To improve the efficiency and effectiveness of image retrieval through advanced feature extraction and selection.
  • To optimize the CBIR process using Particle Swarm Optimization (PSO) and machine learning classifiers.

Main Methods:

  • Extraction of color, grayscale, advanced texture, and shape features from query images.
  • Utilizing contour-based and image moment techniques for shape feature extraction.
  • Employing Particle Swarm Optimization (PSO) for informative feature selection and feature fusion.
  • Training a random forest classifier for target image retrieval and similarity search.

Main Results:

  • The proposed CGATSFRFOPSO method demonstrates efficient image retrieval in large-scale databases.
  • Optimized feature selection and fusion significantly improve matching accuracy.
  • The ensemble of machine learning algorithms enhances the speed of image retrieval.

Conclusions:

  • The developed CBIR approach effectively integrates diverse features (color, gray, texture, shape).
  • PSO-based optimization and random forest classification lead to superior retrieval performance.
  • The research contributes a robust and efficient solution for modern image retrieval challenges.