Shape matching and object recognition using shape contexts
Belongie, S.
Malik, J.
Puzicha, J.
Dept. of Comput. Sci. & Eng., California Univ., San Diego, La Jolla, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 2002
Volume: 24,
Issue: 4
On page(s): 509-522
ISSN: 0162-8828
References Cited: 62
CODEN: ITPIDJ
INSPEC Accession Number: 7241613
Digital Object Identifier: 10.1109/34.993558
Current Version Published: 2002-08-07
Abstract
We present a novel approach to measuring similarity between shapes
and exploit it for object recognition. In our framework, the measurement
of similarity is preceded by: (1) solving for correspondences between
points on the two shapes; (2) using the correspondences to estimate an
aligning transform. In order to solve the correspondence problem, we
attach a descriptor, the shape context, to each point. The shape context
at a reference point captures the distribution of the remaining points
relative to it, thus offering a globally discriminative
characterization. Corresponding points on two similar shapes will have
similar shape contexts, enabling us to solve for correspondences as an
optimal assignment problem. Given the point correspondences, we estimate
the transformation that best aligns the two shapes; regularized
thin-plate splines provide a flexible class of transformation maps for
this purpose. The dissimilarity between the two shapes is computed as a
sum of matching errors between corresponding points, together with a
term measuring the magnitude of the aligning transform. We treat
recognition in a nearest-neighbor classification framework as the
problem of finding the stored prototype shape that is maximally similar
to that in the image. Results are presented for silhouettes, trademarks,
handwritten digits, and the COIL data set
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