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Local tetra patterns: a new feature descriptor for content-based image retrieval.

Subrahmanyam Murala1, R P Maheshwari, R Balasubramanian

  • 1Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, India. subbumurala@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 20, 2012
PubMed
Summary

This study introduces local tetra patterns (LTrPs) for improved content-based image retrieval (CBIR). The novel LTrP algorithm enhances image retrieval accuracy compared to existing methods like local binary patterns (LBP).

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Area of Science:

  • Computer Science
  • Image Processing
  • Pattern Recognition

Background:

  • Content-based image retrieval (CBIR) relies on feature extraction.
  • Existing methods like Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) use gray-level differences.
  • There is a need for more robust and accurate image retrieval techniques.

Purpose of the Study:

  • To propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs).
  • To enhance the efficiency and accuracy of CBIR systems.
  • To introduce a generic strategy for computing nth-order LTrP.

Main Methods:

  • Developed a novel algorithm using local tetra patterns (LTrPs).
  • LTrPs encode pixel relationships based on first-order vertical and horizontal derivatives.

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  • Combined LTrPs with Gabor transform for analysis and evaluated using benchmark databases (Corel 1000, Brodatz, MIT VisTex).

    • Main Results:

    • The proposed LTrP method demonstrated improved retrieval performance over LBP, local derivative patterns, and LTP.
    • Achieved higher average precision/recall on the Corel 1000 database (75.9%/48.7% vs. 70.34%/44.9%).
    • Showcased significant improvements in average retrieval rates on Brodatz (85.30% vs. 79.97%) and MIT VisTex (90.02% vs. 82.23%) databases.

    Conclusions:

    • Local tetra patterns (LTrPs) offer a superior approach for image indexing and retrieval compared to traditional methods.
    • The proposed algorithm effectively enhances CBIR system performance.
    • The LTrP method, especially when combined with Gabor transform, provides a robust solution for image retrieval tasks.