  Not identical but similar objects are everywhere in the world. Examples
include four-legged animals such as dogs and cats, cars of different models,
akin flowers in various colors, and countless others. In this study, we address
a novel task of matching such non-identical objects. We propose a simple
weighting scheme of descriptors that enhances various sparse image matching
methods, which were originally designed for matching identical objects captured
from different perspectives, and achieve semantically robust matching. The
experiments show successful matching between non-identical objects in various
cases including domain shift. Further, we present a first evaluation of the
robustness of the image matching methods under common corruptions, which is a
sort of domain shift, and the proposed method improves the matching in this
case as well.
