Texture retrieval based on multivariate Log-Gaussian mixture model

Abstract

This paper proposed an efficient texture retrieval method for indexing images with heavy-tailed distribution, such as biomedical images, sonar images and natural texture images. In the proposed scheme, a multivariate Log-Gaussian mixture model (MLGMM) was used to model the sharp peaks, heavy tails, and even the multimodal statistical properties of two-dimension Gabor coefficients of texture under different scales and orientations. The parameters of MLGMM are estimated by expectation maximum (EM). In our scheme, each class of texture is modeled by one MLGMM and Bayesian classification is implemented by feeding the output of MMLGM into the Bayesian classifier. Experiments on feature extraction and similarity measurement have been done to demonstrate the effectiveness of our proposed algorithm. Extensive experiments have validated that our retrieval scheme has an average retrieval rate of 2% higher than other related texture statistical techniques.

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Data availability

We used public database for experiments, all data can be downloaded from the web.

Code availability

We reconstruct the code downloaded from the web (http://www.pudn.com/Download/item/id/1274555.html) for estimating the parameters of MLGMM. Our code will be opened after our several immediate articles are completed.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant (61762022), the doctoral startup fund of Guizhou Normal University 2017, under Grant 0517075 and the special project of academic new seedling cultivation and innovation exploration in 2017 under Grant [2017]5726.

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XC: project administration. YL: project administration. ZZ: project administration.

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Correspondence to Yunlan Li or Zaihong Zhou.

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Cite this article

Chen, X., Li, Y. & Zhou, Z. Texture retrieval based on multivariate Log-Gaussian mixture model. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02895-6

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Keywords

  • Multivariate Log-Gaussian mixture model
  • Parameters estimation
  • Bayesian classification
  • Gabor filters
  • Texture retrieval