Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

Minh N. Do, Martin Vetterli

Research output: Contribution to journalArticlepeer-review


We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step followed by computing the Kullback-Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal distribution of wavelet coefficients using generalized Gaussian density (GGD) and on the existence a closed form for the KLD between GGDs. The proposed method provides greater accuracy and flexibility in capturing texture information, while its simplified form has a close resemblance with the existing methods which uses energy distribution in the frequency domain to identify textures. Experimental results on a database of 640 texture images indicate that the new method significantly improves retrieval rates, e.g., from 65% to 77%, compared with traditional approaches, while it retains comparable levels of computational complexity.

Original languageEnglish (US)
Pages (from-to)146-158
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number2
StatePublished - Feb 2002


  • Content-based image retrieval
  • Generalized Gaussian density
  • Kullback-Leibler distance
  • Similarity measurement
  • Statistical modeling
  • Texture characterization
  • Texture retrieval
  • Wavelets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition


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