A new approach is presented for obtaining feature matching based on the identified features from images. Features
which are described by high-dimension vectors are first extracted from a set of reference images and stored in a
database, then the correspondence between similar features from different images are established by introducing the
notion of a Min-cost K-flow Problem (MKP), which consists in finding a min-cost flow subject to the constraint that the
flow value is K. The similarity function, which characterizes these vector components, can avoid the errors that come
from different metrics of vectors. Finally, the K-flow is checked to reject ambiguous correspondence bi-directionally and
automatically in accordance with the ratio of the matching cost. Experiments on three image sets demonstrate
encouraging results.
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