A Correlated Bit-Plane Model for Wavelet Subband Histograms and Its Application to Chinese Materia Medica Starch Grains Classification

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. 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.