The fusion of multiple complementary features can effectively improve the performance of texture image retrieval. In this paper, a new texture image retrieval method based on spatial domain and dual tree complex wavelet transform (DT CWT) domain is proposed. For obtaining the local features of texture images, the local binary pattern (LBP) histogram is calculated in the spatial domain, and the LBP histogram of the magnitude subband and the local tetra pattern (LTrP) histogram of the relative phase subband are respectively calculated in the transform domain. Then in the transform domain, the energy of the approximate subband is computed, and the gamma distribution model for the magnitude subband and the von Mises distribution model for the relative phase subband are carried out, and the obtained energy and the estimated model parameters are taken as the global features of texture images. Finally, the relative L1 distance is used as the similarity measurement for the local features, and the normalized Euclidean distance and the KullbackLeibler (K-L) distance with closed form are used as the similarity measurements for energy feature and distribution parameter features, respectively. Experimental results on VisTex and Brodatz databases show that, compared with the existing best methods, the proposed method achieves higher average retrieval rates with 90.72% and 84.12% respectively.
In view of the fact that multiple complementary feature representation can effectively improve the performance of image retrieval, this paper proposes a new texture image retrieval method based on statistical distribution feature fusion in dual-tree complex wavelet transform domain. Firstly, the statistical distribution energy of the coefficients is calculated in the low frequency subband. Then, in the high frequency complex subbands, the magnitude coefficients are modeled as the Weibull distribution and the relative phase coefficients are modeled as the von Mises distribution. Furthermore, the distribution energy and the estimated model parameters are fused into new features. Finally, the similarity measurement adopting optimal weighted sum is used to retrieve the texture images in the VisTex database. The experimental results show that, compared with the existing texture image retrieval approaches, the proposed method has a higher average retrieval rate.
This paper presents a new approach to texture image retrieval based on the statistical modeling in the multidirectional complex transform domain for effectively improving the performance of the retrieval system. Firstly the basic directional filters are selected and used for the complex transform. Then the coefficients of highpass subband are modeled as generalized Gaussian distribution; the magnitude coefficients of complex directional bandpass subbands are modeled as Gamma distribution; and their distribution parameters are efficiently combined with the statistical features of relative phases of complex bandpass subband coefficients. Finally the texture images of VisTex standard database are retrieved using a new combined form of similarity measurements. The experimental results show that, in comparison with the existing texture image retrieval methods, the proposed approach obtains a superior average retrieval rate.