Hassan Masoumi and Ahad Salimi and Nazanin Barhemmat and Babak Gholami
Using Self Organizing Feature Maps for Classification in RGB Images
1702 - 1706
2015
9
7
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10002035
https://publications.waset.org/vol/103
World Academy of Science, Engineering and Technology
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
selforganizing 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.
Open Science Index 103, 2015