Using Self Organizing Feature Maps for Classification in RGB Images
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.
[1] Goodrum, “Image Information Retrieval: An Overview of Current
Research”, Special Issue on Information Science Research, vol. 3, no. 2,
2000.
[2] 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.
[3] Deng, H. and D.A. Clausi,”Gaussian MRF Rotation-Invariant Features
for SAR Sea Ice Classification,” IEEE PAMI, 26(7): pp. 951-955, 2004.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] S. Prabhakar, H. Cheng, J.C. Handley, Z. Fan Y.W. Lin,
“Picturegraphics Color Image Classification,” Proc. of ICIP, pp. 785-
788, 2002.
[9] 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.
[10] S. W. Kuffler and J. G. Nicholls, “From Neuron to Brain,” (Sinauer
Associates, Sunderland, 1976; Mir, Moscow, 1979).
[11] 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.
[12] 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.
[13] 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.
[14] 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. [15] T. Kohonen, “Self-organized formation of topologically correct feature
maps,” Biol. Cybern., vol. 43, pp. 59–69, 1982.
[16] S. Haykin, Neural Networks: A Comprehensive Foundation. New York,
NY: Macmillan, 1994.
[17] J. E. Moody and C. J. Darken, “Fast learning in networks of locally
tuned processing units,” Neural Comput., vol. 1, pp. 281–294, 1989.
[18] R.C. Gonzalez and R.E. Woods. Digital Image Processing using matlab.
Ed.Prentice-Hall, 2004.
[19] Martin T. Hagan, Howard B. Dcmuth, Mark Beale: Neural Network
Design, 2002.
[1] Goodrum, “Image Information Retrieval: An Overview of Current
Research”, Special Issue on Information Science Research, vol. 3, no. 2,
2000.
[2] 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.
[3] Deng, H. and D.A. Clausi,”Gaussian MRF Rotation-Invariant Features
for SAR Sea Ice Classification,” IEEE PAMI, 26(7): pp. 951-955, 2004.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] S. Prabhakar, H. Cheng, J.C. Handley, Z. Fan Y.W. Lin,
“Picturegraphics Color Image Classification,” Proc. of ICIP, pp. 785-
788, 2002.
[9] 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.
[10] S. W. Kuffler and J. G. Nicholls, “From Neuron to Brain,” (Sinauer
Associates, Sunderland, 1976; Mir, Moscow, 1979).
[11] 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.
[12] 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.
[13] 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.
[14] 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. [15] T. Kohonen, “Self-organized formation of topologically correct feature
maps,” Biol. Cybern., vol. 43, pp. 59–69, 1982.
[16] S. Haykin, Neural Networks: A Comprehensive Foundation. New York,
NY: Macmillan, 1994.
[17] J. E. Moody and C. J. Darken, “Fast learning in networks of locally
tuned processing units,” Neural Comput., vol. 1, pp. 281–294, 1989.
[18] R.C. Gonzalez and R.E. Woods. Digital Image Processing using matlab.
Ed.Prentice-Hall, 2004.
[19] Martin T. Hagan, Howard B. Dcmuth, Mark Beale: Neural Network
Design, 2002.
@article{"International Journal of Information, Control and Computer Sciences:70538", author = "Hassan Masoumi and Ahad Salimi and Nazanin Barhemmat and Babak Gholami", title = "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.", keywords = "Classification, SOFM, neural network, RGB images.", volume = "9", number = "7", pages = "1709-5", }
