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Soft Computing

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14-03-2018 | Methodologies and Application

Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform

Authors: Hong-ying Yang, Lin-lin Liang, Can Zhang, Xue-bing Wang, Pan-pan Niu, Xiang-yang Wang

Published in: Soft Computing | Issue 13/2019

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Abstract

In this paper, we proposed a new framework for textured image retrieval, which is based on Weibull statistical distribution and nonsubsampled contourlet transform. Firstly, the image is decomposed into one lowpass subband and several highpass subbands by using nonsubsampled contourlet transform (NSCT). Secondly, Weibull probability distribution is employed to describe the statistical characteristics of the highpass NSCT coefficients, and the Weibull model parameters are utilized to construct a compact texture image feature space. Finally, image similarity measurement is accomplished by using closed-form solutions for the Kullback–Leibler divergences between the Weibull statistical models. Experimental results demonstrate the high efficiency of our textured image retrieval scheme, which can provide better retrieval rates and lower computational cost, in comparison with the state-of-the-art approaches recently proposed in the literature.

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Title: Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform
Authors: Hong-ying Yang
Lin-lin Liang
Can Zhang
Xue-bing Wang
Pan-pan Niu
Xiang-yang Wang
Publication date: 14-03-2018
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 13/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3127-8

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