In order to improve the accuracy of image classification with convolution network model without increasing more computation, a new image deep convolution classification method based on complex network description was proposed. Firstly, the complex network model degree matrices under different thresholds were obtained by using complex network description of image. Then, the feature vector was obtained by deep convolution neural networks based on degree matrix description of image. Finally, the obtained feature vectors were used for image K-Nearest Neighbors ( KNN) classification. The verification experiments were carried out on the ImageNet Large Scale Visual Recognition Challenge 2014 (ILSVRC2014) database. The experimental results show that the proposed model has higher accuracy and fewer iterations.

In view of significance of color image recognition, the method of color image recognition based on data of image features and Deep Belief Network (DBN) was presented. Firstly, data field of color image was constructed in accord with human visual characteristics; secondly, wavelet transforms was applied to describe multi-scale feature of image; finally, image recognition could be made by training unsupervised DBN.The experimental results show that compared with the methods of Adaboost and Support Vector Machine(SVM),classification accuracy is improved by 3.7% and 2.8% respectively and better image recognition is achieved by the proposed method.