Abstract
This paper extends a recent and very appealing approach of computational learning to the field of image analysis. Recent works have demonstrated that the implementation of Artificial Neural Networks (ANN) could be simplified by using a large amount of neurons with random weights. Only the output weights are adapted, with a single linear regression. Supervised learning is very fast and efficient. To adapt this approach to image analysis, the novelty is to initialize weights, not as independent random variables, but as Gaussian functions with only a few random parameters. This creates smooth random receptive fields in the image space. These Image Receptive Fields - Neural Networks (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.
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References
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best Practice for Convolutional Neural Networks Applied to Visual Document Analysis. In: International Conference on Document Analysis and Recognition. IEEE Computer Society, Los Alamitos (2003)
Yang, F., Paindavoine, M.: Implementation of an RBF Neural Network on Embedded Systems: Real-Time Face Tracking and Identity Verification. IEEE Transactions on Neural Networks 14(5), 1162–1175 (2003)
Egmont-Peterson, M., de Ridder, D., Handels, H.: Image Processing with Neural Networks - A Review. Pattern Recognition 35(10), 2279–2301 (2002)
Jaeger, H.: The Echo State Approach to Analysing and Training Recurrent Neural. Technical Report (GMD148), German National Research Center for Information Technology (2001)
Huang, G.B.: Extreme Learning Machine: Theory and Applications. Neurocomputing 70(1-3), 489–501 (2006)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Compagny Inc., New York (1994)
Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.M.: The Amsterdam Library of Object Images. International Journal of Computer Vision 16(1), 103–112 (2005)
Song, X., Muselet, D., Trémeau, A.: Local color descriptor for object recognition across illumination changes. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 598–605. Springer, Heidelberg (2009)
Elazary, L., Itti, L.: A Bayesian model for efficient visual search and recognition. Visual Search and Selective Attention 50(14), 1338–1352 (2010)
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© 2011 Springer-Verlag Berlin Heidelberg
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Daum, P., Buessler, JL., Urban, JP. (2011). Image Receptive Fields Neural Networks for Object Recognition. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_13
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DOI: https://doi.org/10.1007/978-3-642-21738-8_13
Publisher Name: Springer, Berlin, Heidelberg
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