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Convex Joint Graph Matching and Clustering via Semidefinite Relaxations
[article]
2021
arXiv
pre-print
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
arXiv:2110.11335v1
fatcat:3vojpob54zanjmjmo5lk5r6lq4