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<p>Die Seite wurde neu angelegt: „ == Reference == Shinichi Shirakawa and Shiro Nakayama and Tomoharu Nagao: Genetic Image Network for Image Classification. Applications of Evolutionary Comput…“</p> <p><b>Neue Seite</b></p><div><br /> <br /> == Reference ==<br /> Shinichi Shirakawa and Shiro Nakayama and Tomoharu Nagao: Genetic Image Network for Image Classification. Applications of Evolutionary Computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Lecture Notes in Computer Science, Vol. 5484, pp. 395-404, Springer, April 15-17 2009. <br /> <br /> == DOI ==<br /> http://dx.doi.org/10.1007/978-3-642-01129-0_44<br /> <br /> == Abstract ==<br /> Automatic construction methods for image processing proposed till date approximate adequate image transformation from original images to their target images using a combination of several known image processing filters by evolutionary computation techniques. Genetic Image Network (GIN) is a recent automatic construction method for image processing. The representation of GIN is a network structure. In this paper, we propose a method of automatic construction of image classifiers based on GIN, designated as Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network structure. GIN-IC transforms original images to easier-to-classify images using image transformation nodes, and selects adequate image features using feature extraction nodes. We apply GIN-IC to test problems involving multi-class categorization of texture images, and show that the use of image transformation nodes is effective for image classification problems.<br /> <br /> == Extended Abstract ==<br /> <br /> == Bibtex == <br /> <br /> == Used References ==<br /> Cagnoni, S., Lutton, E., Olague, G. (eds.): Genetic and Evolutionary Computation for Image Processing and Analysis. EURASIP Book Series on Signal Processing and Communications, vol. 8. Hindawi Publishing Corporation (2007)<br /> <br /> Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)<br /> <br /> Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Proceedings of the 5th International Conference on Genetic Algorithms (ICGA 1993), pp. 303–309. Morgan Kaufmann, San Francisco (1993)<br /> <br /> Teller, A., Veloso, M.: Algorithm evolution for face recognition: What makes a picture difficult. In: International Conference on Evolutionary Computation, Perth, Australia, pp. 608–613. IEEE Press, Los Alamitos (1995) http://dx.doi.org/10.1109/ICEC.1995.487453<br /> <br /> Teller, A., Veloso, M.: PADO: A new learning architecture for object recognition. In: Ikeuchi, K., Veloso, M. (eds.) Symbolic Visual Learning, pp. 81–116. Oxford University Press, Oxford (1996)<br /> <br /> Zhang, M., Fogelberg, C.G.: Genetic programming for image recognition: An LGP approach. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 340–350. Springer, Heidelberg (2007)<br /> <br /> Lam, B., Ciesielski, V.: Discovery of human-competitive image texture feature extraction programs using genetic programming. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1114–1125. Springer, Heidelberg (2004)<br /> <br /> Aurnhammer, M.: Evolving texture features by genetic programming. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 351–358. Springer, Heidelberg (2007)<br /> <br /> Aoki, S., Nagao, T.: Automatic construction of tree-structural image transformation using genetic programming. In: Proceedings of the 1999 International Conference on Image Processing (ICIP 1999), Kobe, Japan, vol. 1, pp. 529–533. IEEE, Los Alamitos (1999) http://dx.doi.org/10.1109/ICIP.1999.821685<br /> <br /> Nakano, Y., Nagao, T.: 3D medical image processing using 3D-ACTIT; automatic construction of tree-structural image transformation. In: Proceedings of the International Workshop on Advanced Image Technology (IWAIT 2004), Singapore, pp. 529–533 (2004)<br /> <br /> Nakano, Y., Nagao, T.: Automatic construction of moving object segmentation from video images using 3D-ACTIT. In: Proceedings of The 2007 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2007), Montreal, Canada, pp. 1153–1158 (2007)<br /> <br /> Shirakawa, S., Nagao, T.: Genetic image network (GIN): Automatically construction of image processing algorithm. In: Proceedings of the International Workshop on Advanced Image Technology (IWAIT 2007), Bangkok, Thailand (2007)<br /> <br /> Shirakawa, S., Nagao, T.: Feed forward genetic image network: Toward efficient automatic construction of image processing algorithm. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part II. LNCS, vol. 4842, pp. 287–297. Springer, Heidelberg (2007) http://dx.doi.org/10.1007/978-3-540-76856-2_28<br /> <br /> Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)<br /> <br /> Montana, D.J.: Strongly typed genetic programming. Evolutionary Computation 3(2), 199–230 (1995) http://dx.doi.org/10.1162/evco.1995.3.2.199<br /> <br /> Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)<br /> <br /> <br /> <br /> == Links ==<br /> === Full Text === <br /> [extern file]<br /> <br /> [[intern file]]<br /> <br /> === Sonstige Links ===</div>

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