  This work studies the problem of content-based image retrieval, specifically,
texture retrieval. It focuses on feature extraction and similarity measure for
texture images. Our approach employs a recently developed method, the so-called
Scattering transform, for the process of feature extraction in texture
retrieval. It shares a distinctive property of providing a robust
representation, which is stable with respect to spatial deformations. Recent
work has demonstrated its capability for texture classification, and hence as a
promising candidate for the problem of texture retrieval.
  Moreover, we adopt a common approach of measuring the similarity of textures
by comparing the subband histograms of a filterbank transform. To this end we
derive a similarity measure based on the popular Bhattacharyya Kernel. Despite
the popularity of describing histograms using parametrized probability density
functions, such as the Generalized Gaussian Distribution, it is unfortunately
not applicable for describing most of the Scattering transform subbands, due to
the complex modulus performed on each one of them. In this work, we propose to
use the Weibull distribution to model the Scattering subbands of descendant
layers.
  Our numerical experiments demonstrated the effectiveness of the proposed
approach, in comparison with several state of the arts.
