Statistical properties of bit-plane probability model and its application in supervised texture classification

S. K. Choy*, C. S. Tong

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

33 Citations (Scopus)


The modeling of wavelet subband histograms via the product Bernoulli distributions (PBD) has received a lot of interest and the PBD model has been applied successfully in texture image retrieval. In order to fully understand the usefulness and effectiveness of the PBD model and its associated signature, namely, the bit-plane probability (BP) signature on image processing applications, we discuss and investigate some of their statistical properties. These properties would help to clarify the sufficiency of the BP signature to characterize wavelet subbands, which, in turn, justifies its use in real time applications. We apply the BP signature on supervised texture classification problem and experimental results suggest that the weighted rm L1-norm (rather than the standard L1-norm) should be used for the BP signature. Comparative classification experiments show that our method outperforms the current state-of-the-art Generalized Gaussian Density approaches.

Original languageEnglish
Pages (from-to)1399-1405
Number of pages7
JournalIEEE Transactions on Image Processing
Issue number8
Publication statusPublished - Aug 2008

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Bit-plane probabilities (BPs)
  • Generalized Gaussian Density (GGD)
  • Sufficient statistics
  • Texture classification


Dive into the research topics of 'Statistical properties of bit-plane probability model and its application in supervised texture classification'. Together they form a unique fingerprint.

Cite this