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Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train different types, and different resolution image sets and determine the optimal texture different parameter values. Second, we decompose the image into subimages according to the texture difference, extract the energy features of each subimage, and perform classification. Then, the input image feature vector is converted into a one-dimensional vector through the alternating 5-layer convolution and 3-layer pooling of convolutional neural networks. On this basis, different sizes of convolution kernels are used to extract different convolutions of the image features, and then use convolution to achieve the feature fusion of different dimensional convolutions. Finally, through the increase in the number of training and the increase in the amount of data, the network parameters are continuously optimized to improve the classification accuracy in the training set and in the test set. The actual accuracy of the weights is verified, and the convolutional neural network model with the highest classification accuracy is obtained. In the experiment, two image data sets of gems and apples are selected as the experimental data to classify and identify gems and determine the origin of apples. The experimental results show that the average identification accuracy of the algorithm is more than 90%. [ABSTRACT FROM AUTHOR]

In this era of rapid development, the exchanges between countries are increasing rapidly, which leads to the integration of multiculturalism and its impact on the local culture, making it diluted. Taking the plastic art features of the nomadic civilization in the northern grasslands as an example, the plastic art features of the nomadic civilization are very rich, including color, texture, shape, and local characteristics; the use of traditional methods will lead to poor feature effects, and it is difficult to obtain high-level information. There will also be problems with image recognition. With the hot development of deep learning, for these problems, its advantages and characteristics are introduced and applied to the characteristics of plastic arts, and a deep and shallow network is constructed as its input and feature recognition, which solves the problem of image feature recognition. At the same time, the convolution idea is introduced to enlarge its features, which is more conducive to feature recognition, extraction, and analysis. For the neural network model of deep learning, the traditional optimization algorithm is changed to the Adam optimization algorithm, which solves the problem of decreasing accuracy, improves the accuracy of prediction, and makes it more stable. From the final experimental results, it is not difficult to find that the feature algorithm greatly improves the accuracy rate under different noises, and the time consumption of the algorithm operation is also reduced. The traditional algorithm of the deep learning neural network model is changed to the Adam optimization algorithm, which also improves the prediction accuracy and makes it more stable. In the future development, the unsaturated function can be used as the activation function to optimize or change the model feature algorithm to make the model easier to build and have better training effects. [ABSTRACT FROM AUTHOR]

Not only were traditional artificial neural networks and machine learning difficult to meet the processing needs of massive images in feature extraction and model training but also they had low efficiency and low classification accuracy when they were applied to image classification. Therefore, this paper proposed a deep learning model of image classification, which aimed to provide foundation and support for image classification and recognition of large datasets. Firstly, based on the analysis of the basic theory of neural network, this paper expounded the different types of convolution neural network and the basic process of its application in image classification. Secondly, based on the existing convolution neural network model, the noise reduction and parameter adjustment were carried out in the feature extraction process, and an image classification depth learning model was proposed based on the improved convolution neural network structure. Finally, the structure of the deep learning model was optimized to improve the classification efficiency and accuracy of the model. In order to verify the effectiveness of the deep learning model proposed in this paper in image classification, the relationship between the accuracy of several common network models in image classification and the number of iterations was compared through experiments. The results showed that the model proposed in this paper was better than other models in classification accuracy. At the same time, the classification accuracy of the deep learning model before and after optimization was compared and analyzed by using the training set and test set. The results showed that the accuracy of image classification had been greatly improved after the model proposed in this paper had been optimized to a certain extent. [ABSTRACT FROM AUTHOR]