Texture image retrieval using a classification and contourlet-based
  features
Asal Rouhafzay Nadia Baaziz Mohand Said Allili
In this paper, we propose a new framework for improving Content Based Image
Retrieval (CBIR) for texture images. This is achieved by using a new image
representation based on the RCT-Plus transform which is a novel variant of the
Redundant Contourlet transform that extracts a richer directional information
in the image. Moreover, the process of image search is improved through a
learning-based approach where the images of the database are classified using
an adapted similarity metric to the statistical modeling of the RCT-Plus
transform. A query is then first classified to select the best texture class
after which the retained class images are ranked to select top ones. By this,
we have achieved significant improvements in the retrieval rates compared to
previous CBIR schemes.