Ms. Yanan Wei Profile

Yanan Wei

at Shandong Jianzhu Univ

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Publications (2)

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Proceedings Article | 16 February 2022 Paper

Texture image retrieval based on statistical modeling of all subbands in the complex transform domain

Huaijing Qu, Jia Xu, Hengbin Wang, Jiwei Wang, Yanan Wei, Zhisheng Zhang

Proceedings Volume 12083, 1208310 (2022) https://doi.org/10.1117/12.2623178

KEYWORDS: Image retrieval, Statistical modeling, Feature extraction, Data modeling, Wavelets, Wavelet transforms

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Aiming at the problem of imperfect utilization of subband statistical information in multi-scale texture image retrieval, a texture image retrieval method based on statistical modeling of all subband coefficients in the dual-tree complex wavelet transform domain is proposed. Firstly, the low-frequency subband coefficients, the high-frequency subband amplitude coefficients, and the high-frequency subband relative phase coefficients are modeled as mixed Gaussian distribution, Weibull distribution, and wrapped Cauchy distribution, respectively. Then, the model parameters are estimated by the expectation-maximization algorithm and the maximum likelihood method respectively, and the closed-form Cauchy-Schwarz divergence and Kullback-Leibler divergence are used as similarity measures for retrieval. Finally, the simulation experiment was carried out in two standard texture image libraries of Vistex and Brodatz. Experimental results show that the method proposed in this paper has better retrieval performance than existing methods.

Proceedings Article | 27 January 2021 Paper

Fusing local and global features for texture image retrieval

Hengbin Wang, Huaijing Qu, Jiwei Wang, Jia Xu, Yanan Wei

Proceedings Volume 11720, 117200R (2021) https://doi.org/10.1117/12.2589401

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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.

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