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Related Experiment Videos

Texture image retrieval using new rotated complex wavelet filters.

Manesh Kokare1, P K Biswas, B N Chatterji

  • 1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, India. mbkokare@yahoo.com

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 22, 2005
PubMed
Summary

This study introduces a novel texture image retrieval method using dual-tree rotated complex wavelet filters (DT-RCWF) and dual-tree complex wavelet transform (DT-CWT). The approach enhances texture feature extraction, significantly improving retrieval accuracy in image databases.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Existing texture image retrieval systems often lack high accuracy and efficiency.
  • Characterizing oriented textures requires advanced feature extraction methods.

Purpose of the Study:

  • To develop a novel and accurate texture image retrieval system.
  • To improve upon traditional discrete wavelet transform-based approaches.

Main Methods:

  • Design of two-dimensional (2-D) rotated complex wavelet filters (RCWFs) for oriented texture analysis.
  • Joint application of dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree complex wavelet transform (DT-CWT) for enhanced feature extraction.
  • Computation of energy and standard deviation on subbands for texture feature generation.

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Main Results:

  • The proposed DT-RCWF and DT-CWT method extracts texture features in 12 directions, complementing existing methods.
  • Significant improvements in retrieval rates were observed: 69.61% to 77.75% on Database D1 and 64.83% to 82.81% on Database D2.
  • The method maintains comparable computational complexity to traditional approaches.

Conclusions:

  • The joint use of DT-RCWF and DT-CWT offers a superior approach for texture image retrieval.
  • This method provides enhanced accuracy and efficiency for texture analysis and retrieval systems.