A Graduated Assignment Algorithm for Graph Matching
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Clustering techniques for protein surfaces
Pattern Recognition
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SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Image and Vision Computing
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Attributed relational graph matching based on the nested assignment structure
Pattern Recognition
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SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
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Computer Vision and Image Understanding
Graph matching and clustering using kernel attributes
Neurocomputing
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In this paper, we address the problem of comparing and classifying protein surfaces with graph-based methods. Comparison relies on matching surface graphs, extracted from the surfaces by considering concave and convex patches, through a kernelized version of the Softassign graph-matching algorithm. On the other hand, classification is performed by clustering the surface graphs with an EM-like algorithm, also relying on kernelized Softassign, and then calculating the distance of an input surface graph to the closest prototype. We present experiments showing the suitability of kernelized Softassign for both comparing and classifying surface graphs.