Weibull statistical modeling for textured image retrieval using nonsubsampled contourlet transform
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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.
KeywordsTextured image retrieval Nonsubsampled contourlet transform Weibull statistical model Kullback–Leibler divergences
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61472171, 61272416 and 61701212, Project funded by China Postdoctoral Science Foundation No. 2017M621135, and the Natural Science Foundation of Liaoning Province of China under Grant No. 201602463.
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Conflicts of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
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