Abstract
During the last decade, several approaches have been proposed to address detection and recognition problems, by using graphs to represent the content of images. Graph comparison is a key task in those approaches and usually is performed by means of graph matching techniques, which aim to find correspondences between elements of graphs. Graph matching algorithms are highly influenced by cost functions between nodes or edges. In this perspective, we propose an original approach to learn the matching cost functions between graphs’ nodes. Our method is based on the combination of distance vectors associated with node signatures and an SVM classifier, which is used to learn discriminative node dissimilarities. Experimental results on different datasets compared to a learning-free method are promising.
R. de O. Werneck—Thanks to CNPq (grant #307560/2016-3), CAPES (grant #88881.145912/2017-01), FAPESP (grants #2016/18429-1, #2017/16453-5, #2014/12236-1, #2015/24494-8, #2016/50250-1, and #2017/20945-0), and the FAPESP-Microsoft Virtual Institute (#2013/50155-0, #2013/50169-1, and #2014/50715-9) agencies for funding.
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Acknowledgments
Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER, and several Universities, as well as other organizations (see https://www.grid5000.fr).
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de O. Werneck, R., Raveaux, R., Tabbone, S., da S. Torres, R. (2018). Learning Cost Functions for Graph Matching. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_33
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