基于双广义高斯模型和多尺度融合的纹理图像检索方法

doi:10.11999/JEIT160181

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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.

Key words： Texture image retrieval Dual tree complex wavelet Dual-generalized Gaussian distribution Fuzzy set Evidence theory

Received: 01 March 2016 Published: 08 September 2016

PACS:

TP391.4

Fund:

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

Corresponding Authors: XUE Lixia E-mail: xlxzzm@163.com

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	YANG Juan
	LI Yongfu
	WANG Ronggui
	XUE Lixia
	ZHANG Qingyang

Cite this article:

YANG Juan,LI Yongfu,WANG Ronggui等. Texture Image Retrieval Method Based on Dual-generalized Gaussian Model and Multi-scale Fusion[J]. JEIT, 2016, 38(11): 2856-2863.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT160181 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I11/2856

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