Commenced in January 2007
Paper Count: 30172
Using Self Organizing Feature Maps for Classification in RGB Images
Abstract:Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feedforward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on selforganizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107834Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1714
 Goodrum, “Image Information Retrieval: An Overview of Current Research”, Special Issue on Information Science Research, vol. 3, no. 2, 2000.
 N. O’ Connor, E. Cooke, H. Le Borgne, M. Blighe, and T. Adamek, “The aceToolbox: Lowe-Level Audiovisual Feature Extraction for Retrieval and Classification”. Proc. of EWIMT’05, 2005.
 Deng, H. and D.A. Clausi,”Gaussian MRF Rotation-Invariant Features for SAR Sea Ice Classification,” IEEE PAMI, 26(7): pp. 951-955, 2004.
 R. Zhao and W. I. Grosky, Bridging the Semantic Gap in Image Retrieval, Distributed Multimedia Databases: Techniques and Applications, T. K. Shih (Ed.), Idea Group Publishing, Hershey, Pennsylvania, pp. 14-36, 2001.
 J. Luo, and A. Savakis, “Indoor vs Outdoor Classification of Consumer Photographs using Low-level and Semantic Features,” Proc. of ICIP, pp.745-748, 2001.
 A.K. Vailaya, Jain, and H.-J. Zhang, “On Image Classification: City Images vs. Landscapes,” Pattern Recognition Journal, vol. 31, pp 1921- 1936, December, 1998.
 J. Z. Wang, G. Li, and G. Wiederhold, “SIMPLIcity: Semantics sensitive Integrated Matching for Picture LIbraries,” In IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 23, pages 947-963, 2001.
 S. Prabhakar, H. Cheng, J.C. Handley, Z. Fan Y.W. Lin, “Picturegraphics Color Image Classification,” Proc. of ICIP, pp. 785- 788, 2002.
 Hartmann and R. Lienhart,"Automatic Classification of Images on the Web," In Proc of SPIE Storage and Retrieval for Media Databases, pp. 31-40, 2002.
 S. W. Kuffler and J. G. Nicholls, “From Neuron to Brain,” (Sinauer Associates, Sunderland, 1976; Mir, Moscow, 1979).
 S. Bhattacharyya and P. Dutta, “Multiscale Object Extraction with MUSIG and MUBET with CONSENT: A Comparative Study,” Proceedings of KBCS 2004, pp. 100-109, 2004.
 Lusheng Xi, Chaojun Zhu, Pengfei Ding, Bin Feng, Tianlin Hu,” Can defects classification based on improved SOFM neural network”, International Conference on Anti-Counterfeiting, Security and Identification (ASID), pp.1-4,2012.
 Guangrong Li, “Empirical Study on Financial Risk Identification of Chinese Listed Companies Based on ART-2 and SOFM Neural Network Model”, International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp.582-585, 2013.
 Mingwen Zheng, Yanping Zhang, “A Method to Select RBFNN's Center Based on the SOFM Network”, International Conference on Computer Science and Electronics Engineering (ICCSEE), pp.87-89, 2012.
 T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biol. Cybern., vol. 43, pp. 59–69, 1982.
 S. Haykin, Neural Networks: A Comprehensive Foundation. New York, NY: Macmillan, 1994.
 J. E. Moody and C. J. Darken, “Fast learning in networks of locally tuned processing units,” Neural Comput., vol. 1, pp. 281–294, 1989.
 R.C. Gonzalez and R.E. Woods. Digital Image Processing using matlab. Ed.Prentice-Hall, 2004.
 Martin T. Hagan, Howard B. Dcmuth, Mark Beale: Neural Network Design, 2002.