# Haar Wavelet-Based Classification Method for Visual Information Processing Systems
## 1 Introduction
Under the above conditions, these methods have a low reliability and (often) do not meet the requirements of practice since they find local extrema only. This can also occur due to different levels of noise in the data during the training of the classifier and in the working mode of classification. In addition, the quantity of objects and the variance of their parameters in the class may be different during the training of the classifier. Such peculiarities may appear in classification with a complex form of clusters. A number of References are devoted to determining the quality of information technologies and systems based on them. In particular, in [11], the main components of systems quality in terms of the information component--syntactic, semantic, and pragmatic--are highlighted as well as the features of information quality for healthcare, energy, and transport devices. The methodology assumes the presence of 16 attributes, which are used to assess the quality of systems. A similar approach is also implemented in other studies [12; 13]. When designing systems for visual information processing, the required performance must be ensured. In addition, an important direction is the creation of adaptive systems, i.e., those capable of changing their parameters depending on changing surveillance conditions. Moreover, it is necessary to achieve the coordination of the characteristics for the individual procedures of the system. At the same time, some authors [14] state that estimating the parameters of individual procedures when assessing the quality of new or adapted-to-the-applied-task information technologies is a labor-intensive process. To solve such problems by successively coordinating the characteristic procedures, the multicriteria decision analysis (MCDA) can be considered as a legitimate solution [15]. It is also called multi the object optimization (MOO) or post Pareto optimization (PPO) [16]. For this purpose, a wide range of methods has been developed [17]. In practice, approximate methods for solving MCDA problems are also used [18]. Among them, one can single out the main criterion method and the linear convolution method [19]. At the same time, the application of the main criterion method is limited due to the difficulties associated with the choice of the main criterion and limitations [20]. The linear convolution method requires the determination of the weight coefficients necessary to combine partial objective functionals [21]. Experts formulate requirements for speed and classification reliability in different ways in different application areas; that is, the methods of both groups can require additional information from experts in order to formulate and solve various types of constrained scalar optimization problems. To solve a problem by matching the characteristics of individual procedures, a number of studies suggest using the well-known Shannon entropy formula to measure the information content [22]. For example, such an approach has been proposed for choosing the procedures for segmentation [23; 24], clustering [25], and classification [26; 27; 28; 29]. The authors have previously developed a classification method using [PERSON]'s wavelet transform (_WT_) and hyperbolic _WT_ with improved noise immunity and reduced error [30; 31]. In this case, the error in determining the extremum of the objective function during processing with the Haar _WT_ can be high (due to the asymmetry of the objective function). It has been shown that this error can be reduced by processing with the hyperbolic _WT_. In [30], the classification, determining the coefficients of separating surfaces using multistage processing by the Haar WF and hyperbolic WFs, was described. Such an approach enabled us to obtain a set of the nested intervals for these coefficients. However, due to such complex processing, its performance is low.