Statistical wavelet subband characterization based on generalized gamma density and its application in texture retrieval

S. K. Choy, Chong Sze TONG

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)

Abstract

The modeling of image data by a general parametric family of statistical distributions plays an important role in many applications. In this paper, we propose to adopt the three-parameter generalized Gamma density (GΓD) for modeling wavelet detail subband histograms and for texture image retrieval. The advantage of GΓD over the existing generalized Gaussian density (GGD) is that it provides more flexibility to control the shape of model which is critical for practical histogram-based applications. To measure the discrepancy between GΓDs, we use the symmetrized Kullback-Leibler distance (SKLD) and derive a closed form for the SKLD between GΓDs. Such a distance can be computed directly and effectively via the model parameters, making our proposed scheme particularly suitable for image retrieval systems with large image database. Experimental results on the well-known databases reveal the superior performance of our proposed method compared with the current existing approaches.

Original languageEnglish
Article number5272112
Pages (from-to)281-289
Number of pages9
JournalIEEE Transactions on Image Processing
Volume19
Issue number2
DOIs
Publication statusPublished - Feb 2010

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Generalized gamma density
  • Texture retrieval

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