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

doi:10.1117/12.2623178

16 February 2022 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, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 1208310 (2022) https://doi.org/10.1117/12.2623178
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

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.

Citation Download Citation

Huaijing Qu, Jia Xu, Hengbin Wang, Jiwei Wang, Yanan Wei, and Zhisheng Zhang "Texture image retrieval based on statistical modeling of all subbands in the complex transform domain", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 1208310 (16 February 2022); https://doi.org/10.1117/12.2623178

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