%0 Journal Article
	%A Hassan Masoumi and  Ahad Salimi and  Nazanin Barhemmat and  Babak Gholami
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 103, 2015
	%T Using Self Organizing Feature Maps for Classification in RGB Images
	%U https://publications.waset.org/pdf/10002035
	%V 103
	%X 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
	%P 1709 - 1713