The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Path Similarity Skeleton Graph Matching
Abstract
This paper presents a novel framework to for shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the shortest paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we completely ignore the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of shortest paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and occlusion.
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Cites
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
Cited by
Robust Part-Based Hand Gesture Recognition Using Kinect Sensor
Ren, Z., Yuan, J., Meng, J., & Zhang, Z. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2246148 · Cited by 746
Metadata
- Authors
- Bai, X., & Latecki, L. J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2008-07
- Volume
- 30
- Issue
- 7
- Pages
- 1282-1292
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2111569598
- Cited by
- 343
- References
- 1
- FWCI
- 37.23
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bai, X., & Latecki, L. J. (2008). Path Similarity Skeleton Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2007.70769
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
