Abstract
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Graph matching has been widely used in both image
processing and computer vision domain due to its powerful
performance for structural pattern representation. However,
it poses three challenges to image sparse feature matching:
1) the combinatorial nature limits the size of the possible
matches; 2) it is sensitive to outliers because its objective function
prefers more matches; and 3) it works poorly when handling
many-to-many object correspondences, due to its assumption of
one single cluster of true matches. In this paper, we address
these challenges with a unified framework called density
maximization (DM), which maximizes the values of a proposed
graph density estimator both locally and globally. DM leads
to the integration of feature matching, outlier elimination, and
cluster detection. Experimental evaluation demonstrates that
it significantly boosts the true matches and enables graph
matching to handle both outliers and many-to-many object
correspondences. We also extend it to dense correspondence
estimation and obtain large improvement over the state-of-the-art
methods. We further demonstrate the usefulness of our methods
using three applications: 1) instance-level image retrieval;
2) mask transfer; and 3) image enhancement.