Mr. Hengbin Wang Profile

Hengbin Wang

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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.

Proceedings Article | 3 January 2020 Paper

Texture image retrieval based on statistical feature fusion

Hengbin Wang, Huaijing Qu

Proceedings Volume 11373, 113730Y (2020) https://doi.org/10.1117/12.2557177

KEYWORDS: Image retrieval, Feature extraction, Databases, Statistical analysis, Image fusion, Data modeling, Fusion energy, Digital imaging, Image processing, Wavelet transforms

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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.

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