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Local mesh quantized extrema patterns for image retrieval.

L Koteswara Rao1, D Venkata Rao2, L Pratap Reddy3

  • 1Department of Electronics and Communication Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, India.

Springerplus
|July 19, 2016
PubMed
Summary

We introduce local mesh quantized extrema patterns (LMeQEP), a novel feature descriptor for efficient image indexing and retrieval. This method significantly improves retrieval accuracy and performance compared to existing techniques.

Keywords:
Feature extractionImage retrievalLocal extrema patternsLocal quantized patterns

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Area of Science:

  • Computer Science
  • Image Processing
  • Pattern Recognition

Background:

  • Traditional texture analysis methods rely on local quantized patterns and directional extrema.
  • Extracting robust textural information for image retrieval remains a challenge.

Purpose of the Study:

  • To propose a novel feature descriptor, local mesh quantized extrema patterns (LMeQEP), for enhanced image indexing and retrieval.
  • To improve the accuracy and efficiency of image retrieval systems.

Main Methods:

  • Developed LMeQEP by creating a mesh structure from quantized extrema to capture significant textural information.
  • Integrated RGB color histograms with LMeQEP to further enhance system performance.
  • Evaluated the method on benchmark image repositories like MIT VisTex and Corel-1k.

Main Results:

  • The proposed LMeQEP descriptor demonstrated considerable improvement in image retrieval performance.
  • Achieved higher average retrieval rates and average retrieval precision compared to recent techniques.
  • The integration of RGB color histograms further boosted the system's effectiveness.

Conclusions:

  • LMeQEP is an effective feature descriptor for image indexing and retrieval.
  • The proposed method offers a significant advancement over existing image retrieval techniques.
  • Future work could explore variations and applications of LMeQEP in diverse image analysis tasks.