Abstract
Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging.
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For all the qualitative results the interested readers are referred to http://www.cvc.uab.es/~adutta/Research/ProductGraph/res_rw.php.
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Acknowledgements
This work has been partially supported by the Spanish projects TIN2009-14633-C03-03, TIN2011-24631, TIN2012-37475-C02-02, 2010-CONE3-00029 and the PhD scholarship 2013FI_B2 00074.
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Dutta, A., Lladós, J., Bunke, H., Pal, U. (2014). A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting. In: Lamiroy, B., Ogier, JM. (eds) Graphics Recognition. Current Trends and Challenges. GREC 2013. Lecture Notes in Computer Science(), vol 8746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44854-0_2
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