  An end-to-end trainable ConvNet architecture, that learns to harness the
power of shape representation for matching disparate image pairs, is proposed.
Disparate image pairs are deemed those that exhibit strong affine variations in
scale, viewpoint and projection parameters accompanied by the presence of
partial or complete occlusion of objects and extreme variations in ambient
illumination. Under these challenging conditions, neither local nor global
feature-based image matching methods, when used in isolation, have been
observed to be effective. The proposed correspondence determination scheme for
matching disparate images exploits high-level shape cues that are derived from
low-level local feature descriptors, thus combining the best of both worlds. A
graph-based representation for the disparate image pair is generated by
constructing an affinity matrix that embeds the distances between feature
points in two images, thus modeling the correspondence determination problem as
one of graph matching. The eigenspectrum of the affinity matrix, i.e., the
learned global shape representation, is then used to further regress the
transformation or homography that defines the correspondence between the source
image and target image. The proposed scheme is shown to yield state-of-the-art
results for both, coarse-level shape matching as well as fine point-wise
correspondence determination.
