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Rotation-invariant texture image retrieval using rotated complex wavelet filters.

Manesh Kokare1, P K Biswas, B N Chatterji

  • 1Department of Electronics and Telecommunication Engineering, Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra 431 606, India. mbkokare@sggs.ac.in

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|December 26, 2006
PubMed
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This study introduces a new method for rotation-invariant texture image retrieval using dual-tree rotated complex wavelet filters (DT-RCWF) and DT complex wavelet transform (DT-CWT). The approach significantly enhances retrieval accuracy across various databases, maintaining computational efficiency.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Texture image retrieval is crucial for content-based image analysis.
  • Existing methods often struggle with rotation invariance, limiting their effectiveness on diverse datasets.
  • Discrete wavelet transform (DWT) based approaches have limitations in characterizing oriented textures.

Purpose of the Study:

  • To develop a novel, rotation-invariant method for texture image retrieval.
  • To improve the accuracy and robustness of texture feature extraction.
  • To evaluate the proposed method against existing approaches on various databases.

Main Methods:

  • Utilizing a set of dual-tree rotated complex wavelet filters (DT-RCWF) and dual-tree complex wavelet transform (DT-CWT) jointly.

Related Experiment Videos

  • Extracting texture features in 12 different directions using nonseparable and oriented 2D RCWFs.
  • Generating robust and efficient isotropic rotationally invariant features from decomposed subbands.

    • Main Results:

    • Significant improvements in retrieval accuracy were observed across four different databases (D1-D4).
    • Accuracy increased from 83.17% to 93.71% on a small nonrotated database (D1).
    • Accuracy improved from 64.17% to 78.93% on a large rotated database (D4), outperforming DWT-based methods.

    Conclusions:

    • The proposed DT-RCWF and DT-CWT joint approach offers superior performance for rotation-invariant texture image retrieval.
    • The method effectively characterizes oriented textures and provides robust feature extraction.
    • The new approach achieves higher retrieval accuracy with comparable computational complexity.