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Fast Fourier Transform01:10

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FIRE: fractal indexing with robust extensions for image databases.

Riccardo Distasi1, Michele Nappi, Maurizio Tucci

  • 1Dipt. di Matematica e Informatica, Univ. di Salerno, Baronissi, Italy. ricdis@unisa.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
Summary

Fractal image encoding offers efficient compression and quality. The proposed FIRE system provides robust image indexing, even with transformations, enhancing large database retrieval.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Fractal image encoding leverages image self-similarities for compression and quality.
  • Fractal methods offer compact and stable signatures suitable for image indexing.
  • Existing fractal indexing systems efficiently handle compressed images, ideal for large databases.

Purpose of the Study:

  • To introduce the FIRE (Fractal Image REtrieval) system.
  • To demonstrate the system's invariance to pixel intensity transformations and geometric isometries.
  • To evaluate FIRE's effectiveness in image compression and retrieval accuracy.

Main Methods:

  • Developed the FIRE system based on fractal image encoding principles.
  • Tested FIRE's invariance to rotations (multiples of pi/2) and reflections.
  • Assessed FIRE's robustness against illumination and color alterations.
  • Conducted experiments to measure compression ratios and retrieval accuracy.

Main Results:

  • The FIRE system demonstrated invariance to specified pixel intensity transformations and geometric isometries.
  • FIRE proved robust against common image alterations like changes in illumination and color.
  • Experimental results confirmed FIRE's effectiveness in achieving both high compression and accurate image retrieval.

Conclusions:

  • The FIRE system offers a practical and robust solution for image indexing and retrieval.
  • Its invariance properties make it suitable for real-world applications with diverse image transformations.
  • FIRE effectively balances compression efficiency with high retrieval accuracy for large image databases.