#
Shape Matching and Object Recognition Using Shape Contexts

**
Serge Belongie,
Jitendra Malik and
Jan Puzicha**

Computer Science Division, University of California at Berkeley,
Berkeley CA 94720

`
sjb@cs.berkeley.edu,
malik@cs.berkeley.edu,
puzicha@cs.berkeley.edu`

**
PAMI April 2002
**

### 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 dataset.

PDF version of this paper (965K).

Supplemental
Material

* Serge Belongie *

Sept. 15, 2001