A fast and effective model for wavelet subband histograms and its application in texture image retrieval

Ming Hong Pi*, Chong Sze TONG, Siu Kai Choy, Hong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

Abstract

This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures.

Original languageEnglish
Pages (from-to)3078-3088
Number of pages11
JournalIEEE Transactions on Image Processing
Volume15
Issue number10
DOIs
Publication statusPublished - Oct 2006

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Bit-plane probabilities
  • Embedded block coding with optimized truncation (EBCOT)
  • Image retrieval
  • JPEG2000
  • Textures
  • Wavelet signatures

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