Abstract:

A high similar image recognition and classification algorithm fusing wavelet transform and convolution neural network is proposed for high similar image recognition and classification in specific fields with small color and texture feature differences. Firstly, image texture featuresare extracted by wavelet transform, and the optimal texture difference parameter threshold is determined by different categories and different resolution image sets. Secondly, the wavelet decomposition method is used to segment the image,extract each subgraph’s energy features, and normalize them. Then, a convolution neural network with 5 convolution layers and 3 pool layers are used to transform the input image texture feature vector into onedimensional vector. Finally, by increasing the training number and the data amount, the network parameters are continuously optimized and the classification accuracy in the training set is improved. The actual accuracy of the weights is verified in the test set, and the convolutional neural network model with the highest classification accuracy is obtained.Eggs and apples are chosen as the experimental data. Whether the eggs are freerange or captive and where are the original places of the apples are identified in the experiments. The experimental results show that the average accuracy rate of the algorithm is above 90%.

Key words: image classification;image , recognition;wavelet transform;convolution neural network