Method for Selecting a Set of Image Files Similar to the Object of Interest

In this research, the scientific novelty is the development and testing of the method. This method will allow combining the high speed of the cascade classifier and the quality of the artificial neural network. As an approbation of the work in this article, we study the set of image files. The set of image files are available in the directory on the disk. Next, we select a set of files. The set of files is similar to the object of interest image files. It means, that we can't identical any objects. Then we rewrite files to the target directory. This set should later be used for training a neural network. Images of the object of interest can be in any orientation and on any background. This non-identity must be recognized by a trained neural network. For the purposes of forming such a set, we will need an identifier that has sufficient accuracy for these purposes. Here we do not need very high accuracy. The very identification error of this preliminary analyzer helps us of introducing some dissimilarity between the objects of the images. The task is complicated by the fact that objects can have different sizes (scale) and orientation in space. Our approach based on cascade classifiers and neural networks combines the high speed of the cascade classifier and the quality of the artificial neural network. And the stage of preparing training data for the network is of great importance.