Texture classification using refined histogram

L. Li*, Chong Sze TONG, S. K. Choy

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

69 Citations (Scopus)


In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients, and the concatenation of the RH model parameters for all wavelet subbands forms the so-called RH signature. To justify the usefulness of the RH signature, we discuss and investigate some of its statistical properties. These properties would clarify the sufficiency of the signature to characterize the wavelet subband information. In addition, we shall also present an efficient RH signature extraction algorithm based on the coefficient-counting technique, which helps to speed up the overall classification system performance. We apply the RH signature to texture classification using the well-known databases. Experimental results show that our proposed RH signature in conjunction with the use of symmetrized KullbackLeibler divergence gives a satisfactory classification performance compared with the current state-of-the-art methods.

Original languageEnglish
Article number5398920
Pages (from-to)1371-1378
Number of pages8
JournalIEEE Transactions on Image Processing
Issue number5
Publication statusPublished - May 2010

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Histogram
  • Statistical modeling
  • Texture classification


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