A correlated bit-plane model for wavelet subband histograms and its application to Chinese materia medica starch grains classification

S. K. Choy, Chong Sze TONG

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

This paper presents an effective statistical model for wavelet high frequency subband histograms and a novel image signature by bit-plane extractions. Our proposed model, namely, the first order correlated bit-plane probability model, is shown to match well with the observed histograms especially when the size of subband coefficients is small and performs better than the product Bernoulli distributions (PBD model) as described in [6]. Experimental results on supervised Chinese Materia Medica starch grains images classification show that our proposed signature based on wavelet subband correlated bit-plane probabilities outperforms the current state-of-the-art signatures including the generalized Gaussian density signature (GGD), the granulometric circular size distribution, and the bit-plane probability (BP) signature.

Original languageEnglish
Title of host publicationProceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007
Pages542-548
Number of pages7
DOIs
Publication statusPublished - 2007
Event3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems, SITIS'07 - Jiangong Jinjiang, Shanghai, China
Duration: 16 Dec 200718 Dec 2007

Publication series

NameProceedings - International Conference on Signal Image Technologies and Internet Based Systems, SITIS 2007

Conference

Conference3rd IEEE International Conference on Signal Image Technologies and Internet Based Systems, SITIS'07
Country/TerritoryChina
CityJiangong Jinjiang, Shanghai
Period16/12/0718/12/07

Scopus Subject Areas

  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Signal Processing

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