Abstract
Despite the success of deep learning on representing
images for particular object retrieval, recent studies show
that the learned representations still lie on manifolds in a
high dimensional space. This makes the Euclidean nearest
neighbor search biased for this task. Exploring the manifolds online remains expensive even if a nearest neighbor
graph has been computed offline.
This work introduces an explicit embedding reducing
manifold search to Euclidean search followed by dot product similarity search. This is equivalent to linear graph filtering of a sparse signal in the frequency domain. To speed
up online search, we compute an approximate Fourier basis
of the graph offline. We improve the state of art on particular object retrieval datasets including the challenging Instre
dataset containing small objects. At a scale of 105
images,
the offline cost is only a few hours, while query time is comparable to standard similarity search