# Haar Wavelet-Based Classification Method for Visual Information Processing Systems
## 5 Conclusions  
The authors proposed an improved method of classification by training and determining the set of the nested coefficient value ranges for separating surfaces using the Haar \(WT\) with the reduced number of coefficient ranges and superior performance. To estimate the informativeness, the performance, average classification risk, and Shannon entropy were evaluated.  
It was experimentally proven that when dividing the training sample into two classes, the time for determining one range was reduced to 0.1 s, which is more than two times faster than in existing methods.  
In addition, the results of the study confirmed that the time spent determining the entire set of nested ranges of coefficients depends on the ratio of the variance in the data sample for the training mode and the variance in the working mode of classification. Thus, for the widest range, it can be several times less than for subsequent ranges.  
It is shown that the values of the efficiency indicator for the classification procedure for the test sample differ by units of percent when it is investigated using the informativeness estimation. Therefore, if the requirements to the performance of the system for visual information processing are high, the classification can be run by choosing the value of the coefficient from the range \(\Delta_{1}c_{1}=[1.0,2.36]\) based on pragmatic considerations.  
Thus, the proposed method, firstly, can be applied to select the parameters of the classifier at the debugging stage, taking into account the required level of reliability and informativeness. Secondly, it can be recommended for use in a wide range of applied systems for visual information processing.  
Moreover, wavelet transform methods are one of the promising approaches to analyzing signals containing areas of non-stationarity and intervals of either slowly changing or jumping changes or high-frequency pulsations. Therefore, the proposed method can find wide application in the processing of medical signals and images, in the non-destructive control and monitoring of vibrodiagnostics of machines and equipment, and in steganographic systems of data transmission and protection. It can also be applied in many areas of physics, including molecular dynamics, astrophysics, seismic geophysics, optics, and quantum mechanics.  
As a direction for further applied research, we expect future researchers to employ the proposed wavelet method of effective surface separation for the optimal selection of training samples when using machine-learning methods to increase the accuracy and speed of processing and classifying large streams of data (images) during the real-time analysis of physical processes.  
Conceptualization, G.S.; methodology, G.S. and A.S.; software, G.S. and N.V.; validation, G.S. and N.V.; formal analysis, A.S. and B.R.; investigation, G.S. and N.V.; writing--original draft preparation, G.S.; writing--review and editing, W.H., G.S., A.S., L.Y., B.R. and A.M.; visualization, G.S. and N.V.; supervision, A.S.; project administration, A.S.; funding acquisition, all authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 61772180, the Key R & D plan of Hubei Province (2020 BHB004, 2020 BAB012).  
Not applicable.  
The authors declare no conflict of interest.