Abstract
We propose a new error measure for matching pixels that
is based on co-occurrence statistics. The measure relies
on a co-occurrence matrix that counts the number of times
pairs of pixel values co-occur within a window. The error
incurred by matching a pair of pixels is inversely proportional to the probability that their values co-occur together,
and not their color difference. This measure also works with
features other than color, e.g. deep features. We show that
this improves the state-of-the-art performance of template
matching on standard benchmarks.
We then propose an embedding scheme that maps the input image to an embedded image such that the Euclidean
distance between pixel values in the embedded space resembles the co-occurrence statistics in the original space.
This lets us run existing vision algorithms on the embedded
images and enjoy the power of co-occurrence statistics for
free. We demonstrate this on two algorithms, the LucasKanade image registration and the Kernelized Correlation
Filter (KCF) tracker. Experiments show that performance
of each algorithm improves by about 10%.