In this paper, feature point matching is formulated as an
optimization problem in which the uniqueness condition is
constrained. We propose a novel score function based on
homography-induced pairwise constraints, and a novel optimization
algorithm based on relaxation labeling. Homographyinduced
pairwise constraints are effective for image pairs
with viewpoint or scale changes, unlike previous pairwise
constraints. The proposed optimization algorithm searches
for a uniqueness-constrained solution, while the original
relaxation-labeling algorithm is appropriate for finding manyto-
one correspondences. The effectiveness of the proposed
method is shown by experiments involving image pairs with
viewpoint or scale changes in addition to repeated textures
and nonrigid deformation. The proposed method is also
applied to object recognition, giving some promising results.