Convex Joint Graph Matching and Clustering via Semidefinite Relaxations

@article{Krahn2021ConvexJG,
  title={Convex Joint Graph Matching and Clustering via Semidefinite Relaxations},
  author={Maximilian Krahn and Florian Bernard and Vladislav Golyanik},
  journal={2021 International Conference on 3D Vision (3DV)},
  year={2021},
  pages={1216-1226}
}
This paper proposes a new algorithm for simultaneous graph matching and clustering. For the first time in the literature, these two problems are solved jointly and synergetically without relying on any training data, which brings advantages for identifying similar arbitrary objects in compound 3D scenes and matching them. For joint reasoning, we first rephrase graph matching as a rigid point set registration problem operating on spectral graph embeddings. Consequently, we utilise efficient… 

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