Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval

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.