Image Retrieval via Kullback Divergence of Patches of Wavelets Coefficients in the k-NN Framework
published: Dec. 5, 2008, recorded: November 2008, views: 4973
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
This talk presents a framework to define an objective measure of the similarity (or dissimilarity) between two images for image processing. The problem is twofold:
- define a set of features that capture the information contained in the image relevant for the given task and
- define a similarity measure in this feature space.
In this paper, we propose a feature space as well as a statistical measure on this space. Our feature space is based on a global description of the image in a multiscale transformed domain. After decomposition into a Laplacian pyramid, the coefficients are arranged in intrascale/ interscale/interchannel patches which reflect the dependencies of neighboring coefficients in presence of specific structures or textures. At each scale, the probability density function (pdf) of these patches is used as a description of the relevant information. Because of the sparsity of the multiscale transform, the most significant patches, called Sparse Multiscale Patches (SMP), describe efficiently these pdfs.
We propose a statistical measure (the Kullback-Leibler divergence) based on the comparison of these probability density function. Interestingly, this measure is estimated via the nonparametric, k-th nearest neighbor framework without explicitly building the pdfs. This framework is applied to a query-by-example image retrieval method. Experiments on two publicly available databases showed the potential of our SMP approach for this task. In particular, it performed comparably to a SIFT-based retrieval method and two versions of a fuzzy segmentation-based method (the UFM and CLUE methods), and it exhibited some robustness to different geometric and radiometric deformations of the images.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !