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Color image indexing using BTC.

Guoping Qiu1

  • 1School of Computer Science, The University of Nottingham, Nottingham NG8 1BB, UK. qiu@cs.nott.ac.uk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study introduces block truncation coding (BTC) for image retrieval. New features derived from compressed data enable efficient content-based image retrieval, comparable to existing methods.

Area of Science:

  • Computer Vision
  • Image Processing
  • Information Retrieval

Background:

  • Block Truncation Coding (BTC) is a known image compression technique.
  • Existing methods for content-based image retrieval (CBIR) often require significant computational resources or complex feature extraction.

Purpose of the Study:

  • To explore a novel application of Block Truncation Coding (BTC) for content-based image retrieval.
  • To develop new image features directly from BTC-compressed data for efficient retrieval.
  • To evaluate the performance of these new features against state-of-the-art CBIR techniques.

Main Methods:

  • Utilizing Block Truncation Coding (BTC) for color image compression.
  • Deriving image content description features, specifically the block color co-occurrence matrix (BCCM) and block pattern histogram (BPH), directly from the BTC compressed stream without decoding.

Related Experiment Videos

  • Employing BCCM and BPH to calculate image similarity measures for retrieval.

    • Main Results:

    • Demonstrated that BTC can be effectively applied to content-based image retrieval, not just compression.
    • Introduced BCCM and BPH as novel features for image retrieval derived from compressed data.
    • Experimental results show BCCM and BPH achieve performance comparable to existing state-of-the-art retrieval techniques.

    Conclusions:

    • Block Truncation Coding (BTC) offers a viable and efficient approach for content-based image retrieval.
    • The derived BCCM and BPH features provide a competitive alternative for image similarity assessment in retrieval systems.
    • This method offers a streamlined approach by integrating compression and feature extraction from a single process.