Abstract
The image matching is an important technique in the image processing and the method using Pulse Coupled Neural Network (PCNN) had been proposed. One of the useful feature of the method is that the method is valid for the image matching among rotated, magnified and shrunk images. We have been proposed the parameter learning method of the PCNN for the image matching. Considering that the image matching technique will utilize for any advanced image processing such as a content based image retrieval, the capacity and the versatility of the method are important characteristics to evaluate the method. In this study, our method is tested using total 17,920 images and we describe the characteristics of the capacity and the versatility of image matching method using PCNN with our parameter learning algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Echorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neural Computation 2, 293–307 (1990)
Kruse, W., Eckhorn, R.: Inhibition of sustained gamma oscillations (35-80 Hz) by fast transient responses in cat visual cortex. Proc. Natl. Acad. Sci. USA 93, 6112–6117 (1996)
Echorn, R.: Neural Mechanisms of Scene Segmentation: Recording from the Visual Cortex Suggest Basic Circuits for Liking Field Model. IEEE Trans. Neural Network 10(3), 464–479 (1999)
Johnson, J.L., Padgett, M.L.: PCNN Models and Applications. IEEE Trans. Neural Network 10(3), 480–498 (1999)
Kurokawa, H., Kaneko, S., Yonekawa, M.: A Color Image Segmentation Using Inhibitory Connected Pulse Coupled Neural Network. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part II. LNCS, vol. 5507, pp. 776–783. Springer, Heidelberg (2009)
Kurokawa, H., Yoshihara, M., Yonekawa, M.: An Effect of Inhibitory Connections on Synchronous Firing Assembly in the Inhibitory Connected Pulse Coupled Neural Network. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 179–187. Springer, Heidelberg (2010)
Lindblad, T., Kinser, J.M.: Image processing using Pulse-Coupled Neural Networks, 2nd edn. Springer (2005)
Gu, X.-D., Wang, Y.-y., Zhang, L.-M.: Object Detection Using Unit-Linking PCNN Image Icons. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006, Part II. LNCS, vol. 3972, pp. 616–622. Springer, Heidelberg (2006)
Mahgoub, A.G., Ebeid, A.A., Abdel-Baky, H.M., El-Badawy, E.A.: An Intersecting Cortical Model Based Framework for Human Face Recognition. Journal of Systemics, Cybernetics and Informatics 6(2), 88–93 (2008)
Yonekawa, M., Kurokawa, H.: An Automatic Parameter Adjustment Method of Pulse Coupled Neural Network for Image Segmentation. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009, Part I. LNCS, vol. 5768, pp. 834–843. Springer, Heidelberg (2009)
Vega-Pineda, J., Chacon-Murguia, M.I., Camarillo-Cisneros, R.: Synthesis of Pulse-Coupled Neural Networks in FPGAs for Real-Time Image Segmentation. In: Proc. of IJCNN, pp. 8167–8171 (2006)
Yonekawa, M., Kurokawa, H.: The Parameter Optimization of the Pulse Coupled Neural Network for the Pattern Recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 110–113. Springer, Heidelberg (2010)
Yonekawa, M., Kurokawa, H.: An Evaluation of the Image Recognition Method Using Pulse Coupled Neural Network. In: Honkela, T. (ed.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 217–224. Springer, Heidelberg (2011)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2), Article 5 (2008)
Rui, Y., Huang, T.S.: Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation 10, 39–62 (1999)
Veltkamp, R.C., Tanase, M.: Content-Based Image Retrieval Systems: A Survey. TR UU-CS-2000-34, pp. 1–62 (2002)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: Proc. of Computer Vision and Pattern Recognition Workshop, pp. 178–178 (2004)
Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM - content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21(13-14), 1199–1207 (2000)
Yonekawa, M., Kurokawa, H.: The Content-Based Image Retrieval using the Pulse Coupled Neural Network. In: Proc. of WCCI (to be published, 2012)
Wright, A.H.: Genetic Algorithms for Real Parameter Optimization. In: Foundations of Genetic Algorithms, pp. 205–218 (1999)
CALTECH256, http://www.vision.caltech.edu/Image_Datasets/Caltech256/images/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ishida, Y., Yonekawa, M., Kurokawa, H. (2012). The Capacity and the Versatility of the Pulse Coupled Neural Network in the Image Matching. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-33269-2_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
Online ISBN: 978-3-642-33269-2
eBook Packages: Computer ScienceComputer Science (R0)