Using Self Organizing Feature Maps For Classification In Rgb Images

• Main Authors: Masoumi, Hassan, Ahad Salimi, Barhemmat, Nazanin, Gholami, Babak
• Format: Text
• Language: English
• Published: Zenodo 2015
• Subjects: Classification; SOFM; neural network; RGB images.; Beale; Gonzalez; Hagan; Lowe; Sea ice
• Online Access: https://dx.doi.org/10.5281/zenodo.1107834; https://zenodo.org/record/1107834

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. : {"references": ["Goodrum, \"Image Information Retrieval: An Overview of Current\nResearch\", Special Issue on Information Science Research, vol. 3, no. 2,\n2000.", "N. O' Connor, E. Cooke, H. Le Borgne, M. Blighe, and T. Adamek,\n\"The aceToolbox: Lowe-Level Audiovisual Feature Extraction for\nRetrieval and Classification\". Proc. of EWIMT'05, 2005.", "Deng, H. and D.A. Clausi,\"Gaussian MRF Rotation-Invariant Features\nfor SAR Sea Ice Classification,\" IEEE PAMI, 26(7): pp. 951-955, 2004.", "R. Zhao and W. I. Grosky, Bridging the Semantic Gap in Image\nRetrieval, Distributed Multimedia Databases: Techniques and\nApplications, T. K. Shih (Ed.), Idea Group Publishing, Hershey,\nPennsylvania, pp. 14-36, 2001.", "J. Luo, and A. Savakis, \"Indoor vs Outdoor Classification of Consumer\nPhotographs using Low-level and Semantic Features,\" Proc. of ICIP,\npp.745-748, 2001.", "A.K. Vailaya, Jain, and H.-J. Zhang, \"On Image Classification: City\nImages vs. Landscapes,\" Pattern Recognition Journal, vol. 31, pp 1921-\n1936, December, 1998.", "J. Z. Wang, G. Li, and G. Wiederhold, \"SIMPLIcity: Semantics sensitive\nIntegrated Matching for Picture LIbraries,\" In IEEE Trans. On Pattern\nAnalysis and Machine Intelligence, vol. 23, pages 947-963, 2001.", "S. Prabhakar, H. Cheng, J.C. Handley, Z. Fan Y.W. Lin,\n\"Picturegraphics Color Image Classification,\" Proc. of ICIP, pp. 785-\n788, 2002.", "Hartmann and R. Lienhart,\"Automatic Classification of Images on the\nWeb,\" In Proc of SPIE Storage and Retrieval for Media Databases, pp.\n31-40, 2002.\n[10] S. W. Kuffler and J. G. Nicholls, \"From Neuron to Brain,\" (Sinauer\nAssociates, Sunderland, 1976; Mir, Moscow, 1979).\n[11] S. Bhattacharyya and P. Dutta, \"Multiscale Object Extraction with\nMUSIG and MUBET with CONSENT: A Comparative Study,\"\nProceedings of KBCS 2004, pp. 100-109, 2004.\n[12] Lusheng Xi, Chaojun Zhu, Pengfei Ding, Bin Feng, Tianlin Hu,\" Can\ndefects classification based on improved SOFM neural network\",\nInternational Conference on Anti-Counterfeiting, Security and\nIdentification (ASID), pp.1-4,2012.\n[13] Guangrong Li, \"Empirical Study on Financial Risk Identification of\nChinese Listed Companies Based on ART-2 and SOFM Neural Network\nModel\", International Conference on Intelligent Human-Machine\nSystems and Cybernetics (IHMSC), pp.582-585, 2013.\n[14] Mingwen Zheng, Yanping Zhang, \"A Method to Select RBFNN's Center\nBased on the SOFM Network\", International Conference on Computer\nScience and Electronics Engineering (ICCSEE), pp.87-89, 2012. [15] T. Kohonen, \"Self-organized formation of topologically correct feature\nmaps,\" Biol. Cybern., vol. 43, pp. 59\u201369, 1982.\n[16] S. Haykin, Neural Networks: A Comprehensive Foundation. New York,\nNY: Macmillan, 1994.\n[17] J. E. Moody and C. J. Darken, \"Fast learning in networks of locally\ntuned processing units,\" Neural Comput., vol. 1, pp. 281\u2013294, 1989.\n[18] R.C. Gonzalez and R.E. Woods. Digital Image Processing using matlab.\nEd.Prentice-Hall, 2004.\n[19] Martin T. Hagan, Howard B. Dcmuth, Mark Beale: Neural Network\nDesign, 2002."]}