Color texture analysis using wavelet-based hidden markov model

  • Authors:

  • Ding Siyi;Yang Jie;Xu Qing

  • Affiliations:

  • Inst of Image Processing & Pattern Recognition, Shanghai Jiao tong Univ., Shanghai, Shanghai, P.R China;Inst of Image Processing & Pattern Recognition, Shanghai Jiao tong Univ., Shanghai, Shanghai, P.R China;Inst of Image Processing & Pattern Recognition, Shanghai Jiao tong Univ., Shanghai, Shanghai, P.R China

  • Venue:
  • AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
  • Year:
  • 2004

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Abstract

Wavelet Domain Hidden Markov Model (WD HMM), in particular Hidden Markov Tree (HMT), has recently been proposed and applied to gray level image analysis In this paper, color texture analysis using WD HMM is studied In order to combine color and texture information to one single model, we extend WD HMM by grouping the wavelet coefficients from different color planes to one vector The grouping way is chose according to a tradeoff between computation complexity and effectiveness Besides, we propose Multivariate Gaussian Mixture Model (MGMM) to approximate the marginal distribution of wavelet coefficient vectors and to capture the interactions of different color planes By employing our proposed approach, we can improve the performance of WD HMM on color texture classification The experiment shows that our proposed WD HMM provides an 85% percentage of correct classifications (PCC) on 68 color images from an Oulu Texture Database and outperforms other methods.