Shape matching and object recognition using shape contexts
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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U-Nets extend FCNs by using convolutions to merge information from different scales more accurately before final classification.
“U-Nets [19] extend FCNs by using convolutions to more accurately merge together the information from the different scales before the final classification stage”
From 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks (2018) · cites this paper as [19]
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- Authors
- Belongie, S., Malik, J., & Puzicha, J.
- Affiliations
- University of California San Diego (US)
- University of California, Berkeley (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-04
- Volume
- 24
- Issue
- 4
- Pages
- 509-522
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2057175746
- Cited by
- 6,306
- References
- 65
- Citations by year
- 2026: 49 · 2025: 92 · 2024: 118 · 2023: 113 · 2022: 146 · 2021: 177 · 2020: 180 · 2019: 224 · 2018: 282 · 2017: 348 · 2016: 398 · 2015: 453 · 2014: 487 · 2013: 484 · 2012: 433
- FWCI
- 112.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- UC Berkeley (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- shape matching, object recognition, shape context, point matching, assignment problem, thin-plate splines, shape similarity, nearest neighbor classification, silhouette, trademarks, handwritten digits
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