Content based image retrieval using ripplet transform and Kullback-Leibler Distance

Content-based image retrieval (CBIR), also known
as query by image content is one of the applications
of computer vision. In CBIR, the contents derived from the
image like color, shapes, and textures are analyzed rather than
the metadata such as keywords, tags, and/or descriptions
associated with the image. In this paper, the texture features of
the image are modelled using Generalized Gaussian
Distribution (GGD) and Ripplet Transform. Ripplet transform
has the capability of capturing structural information along
with the curves compared to the traditional discrete wavelet
transform. The similarity between query image and the
training database is found using Kullback-Leibler Distance
between GGDs. The proposed system provides greater
accuracy and flexibility in capturing texture information.
Experimental results on a large image database demonstrate
the efficiency and effectiveness of the proposed CBIR system in
the image retrieval paradigm.