A Probabilistic Approach to Spectral Graph Matching
| Autor: | Amir Egozi, Hugo Guterman, Yosi Keller |
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| Rok vydání: | 2013 |
| Předmět: |
Models
Statistical Matching (graph theory) business.industry Iterative method Applied Mathematics Probabilistic logic Information Storage and Retrieval Estimator Pattern recognition Point set registration Pattern Recognition Automated Computational Theory and Mathematics Artificial Intelligence Data Interpretation Statistical 3-dimensional matching Maximum a posteriori estimation Computer Simulation Computer Vision and Pattern Recognition Artificial intelligence business Algorithms Software Blossom algorithm Mathematics |
| Zdroj: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 35:18-27 |
| ISSN: | 2160-9292 0162-8828 |
| DOI: | 10.1109/tpami.2012.51 |
| Popis: | Spectral Matching (SM) is a computationally efficient approach to approximate the solution of pairwise matching problems that are np-hard. In this paper, we present a probabilistic interpretation of spectral matching schemes and derive a novel Probabilistic Matching (PM) scheme that is shown to outperform previous approaches. We show that spectral matching can be interpreted as a Maximum Likelihood (ML) estimate of the assignment probabilities and that the Graduated Assignment (GA) algorithm can be cast as a Maximum a Posteriori (MAP) estimator. Based on this analysis, we derive a ranking scheme for spectral matchings based on their reliability, and propose a novel iterative probabilistic matching algorithm that relaxes some of the implicit assumptions used in prior works. We experimentally show our approaches to outperform previous schemes when applied to exhaustive synthetic tests as well as the analysis of real image sequences. |
| Databáze: | OpenAIRE |
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