Abstract
The object of research is the processes of identification and classification of objects in computer vision tasks. Currently, for the recognition of images, the best results are demonstrated by artificial neural networks. However, learning neural networks is a poorly conditioned task. Poor conditioning means that even a large data set can carry a small amount of information about a problem that is being solved. Therefore, a key role in the synthesis of parameters of a specific mathematical model of a neural network belongs to educational data. Selection of a representative training set is one of the most difficult tasks in machine learning and is not always possible in practice.The new combined model of image recognition using the non-force interaction theory proposed in the paper has the following key features:– designed to handle large amounts of data;– selects useful information from an arbitrary stream;– allows to naturally add new objects;– tolerant of errors and allows to quickly reprogram the behavior of the system.Compared to existing analogues, the recognition accuracy of the proposed model in all experimental studies was higher than the known recognition methods. The average recognition accuracy of the proposed model was 71.3 %; using local binary patterns – 59.9 %; the method of analysis of the main components – 65.2 %; by the method of linear discriminant analysis – 65.6 %. Such recognition accuracy in combination with computational complexity makes this method acceptable for use in systems operating in conditions close to real time. Also, this approach allows to manage the recognition accuracy. This is achieved by adjusting the number of sectors of the histograms of local binary patterns that are used in the description of images and the number of image fragments used in the classification stage by the introformation approach. To a large extent, the number of image fragments affects the time of classification, since in this case, it is necessary to calculate the matching of the system actions in each of the possible directions in pairs.
Highlights
Every year the volumes of information grow from the formalization and subsequent algorithmic processes that were previously performed manually. 80 % of the information a person receives through vision, so any systems associated with automatic image processing are in demand
This paper presents a generalized model of the combined model of image recognition, in which the image processing process looks like a stream consistently passes through the stages of preprocessing, image description, mapping and classification
Studies show that the theory of non-force interactions, in contrast to the existing classification methods, has the following key features: – designed to handle large amounts of data; – selects useful information from an arbitrary stream; – allows to naturally add new objects; – tolerant of errors and allows to quickly reprogram the behavior of the system
Summary
Every year the volumes of information grow from the formalization and subsequent algorithmic processes that were previously performed manually. 80 % of the information a person receives through vision, so any systems associated with automatic image processing are in demand. 80 % of the information a person receives through vision, so any systems associated with automatic image processing are in demand. One of the key concepts in automatic processing is the concept of object recognition, which is an area of active work over the past twenty years. When algorithms perform recognition at the expert-person level, automation leads to acceleration of data processing systems and increasing their efficiency. There are various methods for image recognition potential functions, Bayesian networks, Markov networks, artificial neural networks, various types of associative memory, and so on. The study of the problem of image recognition has shown that recognition is carried out by methods that do not fully take into account the features of graphic objects, the main ones of which are a small amount of a priori data regarding reference descriptions of recognition objects. It is important to develop a universal model, which makes it possible to assign the object of recognition to a certain class of objects with a small training set with a high probability
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