The positive fuzzy rules often were used only for image classification in the traditional image classification system

while the negative image classification rules were ignored in effect. Nguyen introduced the negative Fuzzy rules into the image classification

proposed a combination of positive and negative fuzzy rules to form the positive and negative fuzzy rule system

and then applied it to remote sensing image/natural image classification. Their experiments proved that their proposed method has achieved good results. However

since their method was realized using the feed forward neural network model which adjust the weights in the gradient descent

the training speed is very slow. Extreme learning machine (ELM) is a single hidden layer feed forward neural network (SLFN) learning algorithm

which has advantages such as quick learning

good generalization performance. In this paper

it proves that Extreme Learning Machine (ELM) and the positive and negative fuzzy rule system is essentially equivalent

so ELM can be naturally used for image classification. Our experimental results support this claim.