TY - JFULL AU - Hassan Masoumi and Ahad Salimi and Nazanin Barhemmat and Babak Gholami PY - 2015/8/ TI - Using Self Organizing Feature Maps for Classification in RGB Images T2 - International Journal of Computer and Information Engineering SP - 1701 EP - 1706 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10002035 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 103, 2015 N2 - 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. ER -