Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval
Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval
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DOI：
10.1109/icip.2008.4711909
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发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
R. Kwitt;A. Uhl
中科院分区：
文献类型：
--
作者：
R. Kwitt;A. Uhl
在这项工作中，我们提出了一种纹理图像检索方法，这是基于测量复小波系数幅度的边缘分布之间的Kullback-Leibler分歧的想法。我们采用Kingsbury的双树复小波变换的图像分解，并建议建模的细节子带系数的幅度由两个参数的Weibull或伽玛分布，我们提供封闭形式的解决方案的Kullback-Leibler分歧。实验结果表明，我们的方法可以实现更高的检索率比经典的方法，使用金字塔离散小波变换与广义高斯模型的细节子带系数。
In this work, we present a texture-image retrieval approach, which is based on the idea of measuring the Kullback-Leibler divergence between the marginal distributions of complex wavelet coefficient magnitudes. We employ Kingsbury's dual-tree complex wavelet transform for image decomposition and propose to model the detail subband coefficient magnitudes by either two-parameter Weibull or Gamma distributions for which we provide closed-form solutions to the Kullback-Leibler divergence. The experimental results indicate that our approach can achieve higher retrieval rates than the classical approach of using the pyramidal discrete wavelet transform together with the generalized Gaussian model for detail subband coefficients.