Texture Image Retrieval Based on Non-Aliasing Contourlet Transform and Hidden Markov Tree Model
Jianwei Yang · Jisuanji gongcheng · 2011
Aiming at spectrum aliasing problems in the directional subbands during the contourlet transform,considering the limitation of the traditional measure KL Distance(KLD) between two hidden Markov models,this paper proposes a texture image retrieval method based on improved KLD,using Non-Aliasing Contourlet Transform(NACT) Hidden Markov Tree(HMT) model.The algorithm uses NACT to decompose a texture,which can deal with the spectrum aliasing phenomenon well,trains the HMT model and takes the HMT model parameter set as the texture features.It computes the similarity between two models using improved KLD,which meets the triangle inequality properties and can measure the distance better.The proposed algorithm is verified by theory and experiment,and results show that the precision of proposed method improves 2.81 percent than that of the CT-HMT combining traditional KLD measure method.