Shape Matching and Object Recognition Using Shape Contexts

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Shape Matching and Object Recognition Using Shape Contexts (2001)

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by Serge Belongie , Jitendra Malik , Jan Puzicha

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	1808 - 21 self

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@ARTICLE{Belongie01shapematching,
    author = {Serge Belongie and Jitendra Malik and Jan Puzicha},
    title = {Shape Matching and Object Recognition Using Shape Contexts},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year = {2001},
    volume = {24},
    pages = {509--522}
}

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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) solv- ing 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 con- texts, 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; reg- ularized 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 trans- form. 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.

Keyphrases

object recognition using shape context shape matching shape context correspondence problem object recognition transformation map point correspondence ularized thin plate spline similar shape con text coil dataset flexible class discriminative characterization stored prototype shape novel approach similar shape reference point optimal assignment problem nearest-neighbor classification framework

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