Título:
Robust multimodal graph matching : sparse coding meets graph matching
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Autor:
Fiori, Marcelo
Sprechmann, Pablo
Volgstein, Joshua
Musé, Pablo
Sapiro, Guillermo
Sprechmann, Pablo
Volgstein, Joshua
Musé, Pablo
Sapiro, Guillermo
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Procesamiento de Señales
Año de publicación:
2013
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Resumen:
Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity formulations, as a non-smooth convex optimization problem that can be efficiently solved using augmented Lagrangian techniques. The method can deal with weighted or unweighted graphs, as well as multimodal data, where different graphs represent different types of data. The proposed approach is also naturally integrated with collaborative graph inference techniques, solving general network inference problems where the observed variables, possibly coming from different modalities, are not in correspondence. The algorithm is tested and compared with state-of-the-art graph matching techniques in both synthetic and real graphs. We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data. The code is publicly available.
Descripción:
Trabajo presentado a 26th International Conference on Neural Information Processing Systems, 2013.
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Citación:
Fiori, M, Sprechmann, P, Vogelstein, J, Musé, P, Sapiro, G. "Robust multimodal graph matching: sparse coding meets graph matching" Publicado en: Proceedings of the 26th International Conference on Neural Information Processing Systems. NIPS 2013, v.1, 2013.
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https://hdl.handle.net/20.500.12008/41764
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| FSVMS13.pdf | — | 419.81 KB | Adobe PDF |