@article{(Open Science Index):https://publications.waset.org/pdf/9822,
	  title     = {A New Method for Image Classification Based on Multi-level Neural Networks},
	  author    = {Samy Sadek and  Ayoub Al-Hamadi and  Bernd Michaelis and  Usama Sayed},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {3},
	  number    = {9},
	  year      = {2009},
	  pages     = {1675 - 1678},
	  ee        = {https://publications.waset.org/pdf/9822},
	  url   	= {https://publications.waset.org/vol/33},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 33, 2009},
	}