Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion

YANG Juan; LI Yongfu; WANG Ronggui; XUE Lixia; ZHANG Qingyang

doi:10.11999/JEIT160181

Volume 38 Issue 11

Dec. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2856-2863

YANG Juan, LI Yongfu, WANG Ronggui, XUE Lixia, ZHANG Qingyang. Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2856-2863. doi: 10.11999/JEIT160181

Citation:

YANG Juan, LI Yongfu, WANG Ronggui, XUE Lixia, ZHANG Qingyang. Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2856-2863. doi: 10.11999/JEIT160181

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Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion

doi: 10.11999/JEIT160181

Funds:

China Postdoctoral Fund (2014M561817), The Natural Science Foundation of Anhui Province (J2014AKZR 0055)

Received Date: 2016-03-01
Rev Recd Date: 2016-07-01
Publish Date: 2016-11-19

Abstract

Abstract

Texture factor is one of the most important characteristics in the image description. In order to describe the texture feature accurately, and enhance image distinguish ability, a method of texture image retrieval is proposed based on Dual-Tree Complex Wavelet Transform (DT-CWT) in this paper. Firstly, each sub-band coefficient is obtained by DT-CWT, because the coefficient distribution exists slight incomplete symmetrical feature, which is modeled as dual-generalized Gaussian model. Secondly, there is incomplete independent and uncertain conflict between the sub-band coefficients, therefore the Fuzzy Set and Dempster-Shafer (FS-DS) evidence theory are applied to blending the characteristics of each subband coefficients. The performance of the propose algorithm is tested on the Brodatz and color texture image library, and also compared with a variety of statistical modeling methods. The experimental results demonstrate that the proposed method can improve the average retrieval rate of the texture images effectively.
- Texture image retrieval,
- Dual tree complex wavelet,
- Dual-generalized Gaussian distribution,
- Fuzzy set,
- Evidence theory

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References(24)

References

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