A New Method for Image Classification Based on Multi-level Neural Networks
In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.
[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, Nov. 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: Semanticssensitive
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] Yu, H., Li, M., Zhang, H.-J., Feng, J., Color texture moments for
content-based image retrieval, In: Internat. Conf. on Image Processing,
vol. 3, pp. 929-932, 2002.
[13] Der-Chiang Li and Yao-Hwei Fang, "An algorithm to cluster data for
efficient classification of support vector machines," Expert Systems with
Applications, vol. 34, pp. 2013-2018, 2008.
[14] R. Marmo et al. "Textural identification of carbonate rocks by image
processing and neural network: Methodology proposal and examples,"
Computers and Geosciences, 31, pp. 649-659, 2005.
[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, Nov. 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: Semanticssensitive
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] Yu, H., Li, M., Zhang, H.-J., Feng, J., Color texture moments for
content-based image retrieval, In: Internat. Conf. on Image Processing,
vol. 3, pp. 929-932, 2002.
[13] Der-Chiang Li and Yao-Hwei Fang, "An algorithm to cluster data for
efficient classification of support vector machines," Expert Systems with
Applications, vol. 34, pp. 2013-2018, 2008.
[14] R. Marmo et al. "Textural identification of carbonate rocks by image
processing and neural network: Methodology proposal and examples,"
Computers and Geosciences, 31, pp. 649-659, 2005.
@article{"International Journal of Electrical, Electronic and Communication Sciences:58888", author = "Samy Sadek and Ayoub Al-Hamadi and Bernd Michaelis and Usama Sayed", title = "A New Method for Image Classification Based on Multi-level Neural Networks", abstract = "In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.", keywords = "Image classification, multi-level neural networks,feature extraction, wavelets decomposition.", volume = "3", number = "9", pages = "1733-4", }
