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Alicia Fornes, Josep Llados and Gemma Sanchez. 2008. Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.52–60. (LNCS.)
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Joan Mas, J.A. Jorge, Gemma Sanchez and Josep Llados. 2008. Representing and Parsing Sketched Symbols using Adjacency Grammars and a Grid-Directed Parser. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities,.176–187. (LNCS.)
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Jose Antonio Rodriguez, Gemma Sanchez and Josep Llados. 2008. Categorization of Digital Ink Elements using Spectral Features. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities. Springer–Verlag, 188–198. (LNCS.)
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Marçal Rusiñol and Josep Llados. 2008. A Region-Based Hashing Approach for Symbol Spotting in Technical Documents. In W. Lius, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.104–113. (LNCS.)
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Jaume Gibert, Ernest Valveny and Horst Bunke. 2011. Dimensionality Reduction for Graph of Words Embedding. In Xiaoyi Jiang, Miquel Ferrer and Andrea Torsello, eds. 8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition.22–31. (LNCS.)
Abstract: The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
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Miquel Ferrer, I. Bardaji, Ernest Valveny, Dimosthenis Karatzas and Horst Bunke. 2013. Median Graph Computation by Means of Graph Embedding into Vector Spaces. In Yun Fu and Yungian Ma, eds. Graph Embedding for Pattern Analysis. Springer New York, 45–72.
Abstract: In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.
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