Ensemble Image Classification Method Based on Genetic Image Network
Inhaltsverzeichnis
Reference
Shiro Nakayama and Shinichi Shirakawa and Noriko Yata and Tomoharu Nagao: Ensemble Image Classification Method Based on Genetic Image Network. Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010, LNCS, Vol. 6021, pp. 313-324, Springer, 7-9 April 2010.
DOI
http://dx.doi.org/10.1007/978-3-642-12148-7_27
Abstract
Automatic construction method for image classification algorithms have been required. Genetic Image Network for Image Classification (GIN-IC) is one of the methods that construct image classification algorithms automatically, and its effectiveness has already been proven. In our study, we try to improve the performance of GIN-IC with AdaBoost algorithm using GIN-IC as weak classifiers to complement with each other. We apply our proposed method to three types of image classification problems, and show the results in this paper. In our method, discrimination rates for training images and test images improved in the experiments compared with the previous method GIN-IC.
Extended Abstract
Bibtex
Used References
Shirakawa, S., Nakayama, S., Nagao, T.: Genetic Image Network for Image Classification. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoCOMNET. LNCS, vol. 5484, pp. 395–404. Springer, Heidelberg (2009)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proceedings of the 13th International Conference on Machine Leaning (ICML 1996), pp. 148–156 (1996)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. 511–518 (2001)
Iba, H.: Bagging, Boosting, and Bloating in Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference 1999 (GECCO 1999), vol. 2, pp. 1053–1060 (1999)
Folino, G., Pizzuti, C., Spezzano, G.: Boosting Technique for Combining Cellular GP Classifiers. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 47–56. Springer, Heidelberg (2004)
Folino, G., Pizzuti, C., Spezzano, G.: GP Ensembles for Large-scale Data Classification. IEEE Transaction on Evolutionary Computation 10(5), 604–616 (2006) http://dx.doi.org/10.1109/TEVC.2005.863627
Mohemmed, A.W., Zhang, M., Johnston, M.: Particle Swarm Optimization Based Adaboost for Face Detection. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 2494–2501. IEEE Press, Los Alamitos (2009) http://dx.doi.org/10.1109/CEC.2009.4983254
Schwenk, H., Bengio, Y.: Boosting Neural Networks. Neural Computation 12(8), 1869–1887 (2000) http://dx.doi.org/10.1162/089976600300015178
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
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)
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision (IJCV) 60(2), 91–110 (2004) http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94
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