Near Duplicate Detection
Chris Pal David Rim
Introduction
We have been working in a collaboration with
Eastman House, in building a Wiki to promote research in photography among museum curators, photography researchers and students. The wiki has broad scope, bringing together expert knowledge about photographers and their work and methods. Automating some of the tasks in bringing together this knowledge is a key component in making the wiki both useful and easy to maintain. It is also has given rise to extensive computer vision and natural language applications. For the past semester, we have focused on image comparison, that is, how to detect duplicate images and specifically, how to detect if images are in fact prints of an identical negative. We have developed an application which detects if images are prints of a negative and then displays the duplicates registered with each other in order to give viewers an idea of how the different print making decisions effect the impact of an image.
Abstract
Image duplicate detection can be achieved via landmark or keypoint extraction
using simple geometric consistency and verication checks. This
can be applied efficiently to the following problem: given a set of images
can we determine which came from the same negative? A Bayesian approach to
matching might be possible if we consider the underlying mechanism to be
a generative process which produces the set of possible matching points
We can imagine that the set of candidate matches are generated by
two independent processes, one of which corresponds to the an affine trans-
formation and a noise generating process. We show that posing this problem in this way
produces a robust and efficient estimate. Furthermore our approach
leads directly to a formulation that is identical to an iterative weighted
least squares procedure, which captures the intuition nicely in a formal way.
Problem
Given two photographs, can we automatically tell if they are prints of an identical negative? This
has applications in the study of photographic images.
Generate a rough set of candidate matches using sum of squared distances
nearest neighbor algorithm.
Compared to standard approaches, the solution recovered by the above is robust
to outliers. We use RANSAC to verify the approach.