Image Recognition Algorithm Based on Evidence Theory and Improved Neural Network

Because single feature automatic image recognition algorithm's identification result is not stable and the correct recognition rate is low blemish,this paper put forward a image automatic recognition algorithm based on based on evidence theory and improved neural network.Firstly,color and texture features of image are extracted which can reflect the image category information,and then RBF neural network is used for single feature identification and recognition results are taken as evidences,the evidence theory is used to fuse the identification results and gets the final recognition result of image.The result of the simulation shows that the proposed algorithm's the average recognition rate is up to 92.29%,and compared with the single feature recognition algorithms,image recognition results of reliability and accuracy is increased greatly,and it has better application prospect in image recognition.