An Image Classification Method Using Hashing Preprocessing

In the study, the authors consider classical classification methods for the applied problem of image recognition. Accuracy, computation time, classifier size, training time for the following methods are considered: “Fully Connected Neural Network”, “Convolutional Neural Network”, “Recurrent Neural Network”, “Decision Tree”, “Gradient Boosted Trees”, “Logistic Regression”, “Markov”, “Naive Bayes”, “Nearest Neighbors”, “Random Forest”, “Support Vector Machine”. A new approach “Neural Network with Hash” is proposed, which represents image preprocessing using polynomial hashing. Collision resolution is performed by a fast method of open addressing. A computer experiment on classification by 10 classes was carried out on a dataset of 600 animal images using the Wolfram mathematical package. For the proposed approach with preprocessing, the results showed the same classification accuracy as the classical methods, and a higher training and computational speed than the “Convolutional Neural Network” and “Recurrent Neural Network”.