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Smart adaptive learning and optimized feature clustering for enhanced image retrieval.

P Umaeswari1, Sujata Patil2, Parameshachari Bidare Divakarachari3

  • 1Department of Computer Science and Business Systems R.M.K. Engineering College, Kavaraipettai, India.

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Summary

This study introduces SEGJO-EDCNN, a novel method for Content-Based Image Retrieval (CBIR). It enhances feature clustering and matching accuracy, achieving superior performance on benchmark datasets.

Keywords:
Content-Based image retrievalConvolutional neural networkElite opposition learningEntropy-based divergence functionGolden Jackal optimizationScaling factor

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Content-Based Image Retrieval (CBIR) faces challenges with vast multimedia data.
  • Accurate feature dissimilarities are crucial for effective CBIR.
  • Existing methods struggle with premature convergence and redundant activations.

Purpose of the Study:

  • To propose a novel approach, SEGJO-EDCNN, for enhanced CBIR.
  • To improve feature clustering and matching accuracy in image retrieval.
  • To address limitations of existing CBIR techniques.

Main Methods:

  • Developed SEGJO (Scaling Factor and Elite Opposition Learning-based Golden Jackal Optimization) for feature clustering.
  • Integrated Scaling Factor (SF) and Elite Opposition Learning (EOL) to enhance search and prevent premature convergence.
  • Utilized Local Binary Pattern, Zernike Moments, and Color Moments for feature extraction.
  • Incorporated an Entropy-based Divergence (ED) function within a Convolutional Neural Network (CNN) named EDCNN for improved matching.

Main Results:

  • SEGJO-EDCNN demonstrated superior performance on Corel 5K and Oxford Flower datasets.
  • Achieved a mean average precision (MAP) of 97.595% on the Corel 5K dataset, outperforming ELNDP and DNN-SAR.
  • Attained a MAP of 99.239% on the Oxford Flower dataset, surpassing SVM-CBIR.

Conclusions:

  • The proposed SEGJO-EDCNN method significantly enhances CBIR performance.
  • The integration of SF, EOL, and EDCNN effectively improves feature clustering and retrieval accuracy.
  • SEGJO-EDCNN offers a robust solution for the growing challenges in large-scale image retrieval.