Abstract
We present three new algorithms to model images with graph primitives. Our main goal is to propose algorithms that could lead to a broader use of graphs, especially in pattern recognition tasks. The first method considers the q-tree representation and the neighbourhood of regions. We also propose a method which, given any region of a q-tree, finds its neighbour regions. The second algorithm reduces the image to a structural grid. This grid is postprocessed in order to obtain a directed acyclic graph. The last method takes into account the skeleton of an image to build the graph. It is a natural generalization of similar works on trees [8, 12]. Experiments show encouraging results and prove the usefulness of the proposed models in more advanced tasks, such as syntactic pattern recognition tasks.
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References
Cychosz, J.M.: Thinning algorithm from the article: Efficient binary image thinning using neighbourhood maps. In: Graphics Gems IV, pp. 465–473. Academic Press (1994)
de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational geometry, pp. 291–306. Springer (2000)
Escolano, F., Giorgi, D., Hancock, E.R., Lozano, M.A., Falcidieno, B.: Flow Complexity: Fast Polytopal Graph Complexity and 3D Object Clustering. In: Torsello, A., Escolano, F., Brun, L. (eds.) GbRPR 2009. LNCS, vol. 5534, pp. 253–262. Springer, Heidelberg (2009)
Flasinski, M., Myslinski, S.: On the use of graph parsing for recognition of isolated hand postures of Polish Sign Language. Pattern Recognition 43, 2249–2264 (2010)
Goodchild, M.: Quadtree algorithms and spatial indexes. Technical Issues in GIS, NCGIA, Core Curriculum 37, 5–6 (1990)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009)
Liu, J., Li, M., Liu, Q., Lu, H., Ma, S.: Image annotation via graph learning. Pattern Recognition 42, 218–228 (2009)
López, D., Piñaga, I.: Syntactic Pattern Recognition by Error Correcting Analysis on Tree Automata. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 133–142. Springer, Heidelberg (2000)
Luque, R.G., Comba, J.L.D., Freitas, C.: Broad-phase collision detection using semi-adjusting bsp-trees. In: ACM i3D, pp. 179–186 (2005)
Newman, M.E.J.: The structure and function of complex networks. SIAM 45 (2003)
Poveda, J., Gould, M.: Multidimensional binary indexing for neighbourhood calculations in spatial partition trees. Comput. Geosci. 31(1), 87–97 (2005)
Rico-Juan, J.R., Micó, L.: Comparison of AESA and LAESA search algorithms using string and tree edit distances. Pattern Recognition Letters 24, 1427–1436 (2003)
Shin, H., Tsuda, K., Schölkopf, B.: Protein functional class prediction with a combined graph. Expert Systems with Applications 36, 3284–3292 (2009)
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Gallego-Sánchez, AJ., Calera-Rubio, J., López, D. (2012). Structural Graph Extraction from Images. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_86
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DOI: https://doi.org/10.1007/978-3-642-28765-7_86
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