Improving the quality of object classification in images by ensemble classifiers with stacking

Abstract

The object of research is the process of classifying objects in images. The quality of classification refers to the ratio of correctly recognized objects to the number of images. One of the options for improving the quality of classification is to increase the depth of neural networks used. The main difficulties along the way are the difficulty of training such neural networks and a large amount of computing that makes it difficult to use them on conventional computers in real time. An alternative way to improve the quality of classification is to increase the width of the neural networks used, by constructing ensemble classifiers with staking. However, they require the use of classifiers at the first stage with different structured processing of input images, characterized by high quality classification and relatively low volume of calculations. The number of known such architectures is limited. Therefore, the problem arises of increasing the number of classifiers at the first stage of the ensemble classifier by modifying known architectures. It is proposed to use blocks of rotation of images at different angles relative to the center of the image. It is shown that as a result of structured image processing by the starting classifier, processing of rotated image leads to redistribution of errors on image set. This effect makes it possible to increase the number of classifiers in the first stage of the ensemble classifier. Numerical experiments have shown that adding two analogs of the MLP-Mixer algorithm to known configurations of ensemble classifiers reduced the error from 1 to 11 % when working with the CIFAR-10 dataset. Similarly, for CCT, the error reduction was between 2.1 and 10 %. In addition, it has been shown that increasing the MLP-Mixer configuration in width gives better results than increasing in depth. A prerequisite for the success of using the proposed approach in practice is the structured image processing by the starting classifier