# Aircraft Cabin Decoration Style Recommendation Algorithm Based on Machine Vision

Authors: Yongbo Yu; Heyang Tong; Denghan Wang
Journal: Security and Communication Networks
 (2021)

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## Abstract

To improve the accuracy of the aircraft cabin decoration style recommendation algorithm, an aircraft cabin decoration style recommendation algorithm based on machine vision is proposed. The outline diagram of aircraft cabin decoration style is described with the help of hog features, and the clustering observation is carried out through K-means clustering algorithm to complete the feature description of aircraft cabin decoration style. The background pixels of aircraft cabin decoration are determined with the help of Gaussian mixture model method, the style feature points to be measured are updated gradient, the input aircraft cabin decoration style sequence image is compared with the background image, the difference of statistical information such as gray characteristics of pixels or straight image is analyzed, and the background preprocessing of aircraft cabin decoration style is completed. This study analyzes the basic principle and operation process of machine vision system, obtains the aircraft cabin decoration style image with the help of machine vision, calculates the weight value of recommended users, determines the number of decoration style recommendations, and completes the aircraft cabin decoration style recommendation. The experimental results show that the proposed method can effectively improve the accuracy of aircraft cabin decoration style recommendations, and it is feasible.


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## Body

## 1. Introduction

With the vigorous development of civil aviation in today’s society, more and more people have begun to choose to travel by air. It is estimated that the global passenger flow by air can exceed one billion people times a year. Although China’s civil aviation started late, the number of passengers has also shown rapid growth in recent ten years. From 80 million person-times a year in the early 21st century to nearly 400 million person-times a year, an increase of almost five times. Although the number of passengers has increased, the number of civil aircraft in China has also changed significantly in recent years [1]. From 982 in 2000 to 4168 in 2014, an increase of more than four times. The continuous growth of passenger flow and passenger plane volume shows that China's demand for the civil aviation industry will be more and more vigorous in the future, and China has entered the fast lane of Civil Aviation Development [2]. Different from the general indoor environment, the cabin environment of passenger aircraft has the particularity of a closed environment. Among them, the interior decoration environment has an important impact on the travel mood of passengers. If the decoration style meets the psychological needs of the public, it will bring a happy journey for tourists. If the public poorly evaluates it, it means that the decoration style is not suitable for the ideal of the public and needs continuous improvement [3]. Therefore, in order to improve the quality of aircraft cabin decoration, relevant researchers have also studied its aircraft cabin decoration style and achieved certain results [4].Wang [5] proposed an aesthetic presentation method of the new Chinese style in interior decoration and decorated the interior environment based on this method. As a designer, the new Chinese style in interior decoration design is an important embodiment of Chinese traditional culture in modern architectural decoration design. It is the integration of Chinese traditional spirit and Chinese traditional architectural decoration design style, which is affected by regional traditional national culture. The application of new Chinese style in interior decoration design makes the design effect. The concept of this method has improved the understanding of China’s interior decoration style, but it is still in the theoretical stage. It needs to be further implemented and applied to the recommendation of decoration style as soon as possible. Wang [6] proposed a reasonable color-matching method of interior decoration materials based on a genetic algorithm applied to decoration style recommendation. In order to solve the problem that the calculation process of traditional methods is extremely complex, the collected information of professional knowledge is incomplete, cannot cover the whole field of color matching, and there is no unique optimal solution, the reasonable color-matching method of interior decoration materials is studied through genetic algorithm. The chromosome in genetic algorithm is composed of three-dimensional components and color for genetic coding, RGB is transformed into HSL, and the color disharmony coefficient is calculated. The harmony degree of color matching of interior decoration materials is fully analyzed, and the visual comfort degree is calculated. Three parts weight the color-matching fitness function of interior decoration materials: color-matching harmony degree, color-matching harmony degree, and visual comfort degree. The individuals with the highest fitness are obtained using the selection, crossover, and mutation operations of genetic algorithm, and the stop iteration conditions are set. The results show that the proposed method has better color comfort. Through the evaluation and investigation of color-matching results, it is found that most customers are very satisfied or satisfied with the evaluation results of the color-matching scheme of interior decoration materials obtained by the proposed method, and the method is simple to operate. It can be seen that the proposed method has reasonable color matching and superior performance. However, this method only considers color matching, and few details should be considered in other decorations, so it has some limitations. Lu [7] proposed research on a 3D, indoor, home-style recommendation method based on a user interest model. In order to better assist in the recommendation of interior decoration style, a three-dimensional interior decoration style recommendation method based on user interest model is proposed. The method includes three-dimensional model data collection, home-style feature definition, and style-association mining. The user interest model is established through user behavior data analysis. The experimental results show that the proposed method can effectively complete the three-dimensional indoor-style analysis, and the recommended results get good user satisfaction. The idea of this method is clear, but there are some deficiencies in the extraction of decoration style data. Further improvements are still needed.Big explosive data pose significant challenges to CF recommendation algorithms because it is becoming quite time consuming and energy consuming. It has to be optimized and accelerated by powerful engines to process on large data scale. Wang et al. [8] proposed WooKong, a ubiquitous accelerator architecture for the collaborative-filtering recommendation on FPGA to solve these problems. Online education and e-learning have vigorously sprung up and produced massive educational data in a streaming way. It is very challenging to acquire the appropriate learning resources and the suitable learning partners from the streaming-updated educational big data. Liu et al. [9] aimed to provide sustainable smart educational services including precise personalized recommendation and adaptive clustering under different contexts by correlatively analyzing the global educational data from multiple dimensions via incremental tensor decomposition.In order to make up for the shortcomings of the above methods, an aircraft cabin decoration style recommendation algorithm based on machine vision is proposed in this study. The outline diagram of aircraft cabin decoration style is described with the help of hog features, and the clustering observation is carried out throughK⁃means clustering algorithm to complete the feature description of aircraft cabin decoration style. The background pixels of aircraft cabin decoration are determined with the help of Gaussian mixture model method, the style feature points to be measured are updated gradient, the input aircraft cabin decoration style sequence image is compared with the background image, the difference of statistical information such as gray characteristics of pixels or straight image is analyzed, and the background preprocessing of aircraft cabin decoration style is completed. This study analyzes the basic principle and operation process of machine vision system, obtains the aircraft cabin decoration style image with the help of machine vision, calculates the weight value of recommended users, determines the number of decoration style recommendations, and completes the aircraft cabin decoration style recommendation.The contributions of this study include the following three points:(1)
To improve the accuracy of the aircraft cabin decoration style recommendation algorithm, an aircraft cabin decoration style recommendation algorithm based on machine vision is proposed.(2)
The outline diagram of aircraft cabin decoration style is described with the help of hog features, and the clustering observation is carried out throughK-means clustering algorithm to complete the feature description of aircraft cabin decoration style.(3)
The experimental results show that the proposed method can effectively improve the accuracy of aircraft cabin decoration style recommendation, and it is feasible.


## 2. Aircraft Cabin Decoration Style Recommendation Algorithm Based on Machine Vision

### 2.1. Description of Characteristics of Aircraft Cabin Decoration Style

In order to improve the effect of aircraft cabin decoration style recommendation algorithm, firstly, the characteristics of aircraft cabin decoration style are described. In this study, the outline of aircraft cabin decoration style is described by HOG features [10], and the following results are obtained:(1)min∑p=1nt2AP−BPVP+μπ2.In the formula,AP represents the HOG characteristic matrix of the P perspective; BP and VP represent the base matrix and coefficient matrix of the P perspective, respectively; μ represents a smoothing factor, and P is the total number of perspectives.Based on this, clustering studies with the standardK-means clustering algorithm [11] obtained the IDF value of each cluster, i.e.,(2)IDFx=c∈E:C∈eN.In the formula,x represents a class cluster, N represents the total number of styles this class contains, N represents the total number of styles contains, and E represents the number of valid components that the style has. The aircraft cabin decoration style characteristics can be completed by selecting the appropriate IDF.In the description of cabin decoration style, the outline of HOG features andK-means clustering algorithm to complete the description.


### 2.2. Aircraft Cabin Trim Style Background Pretreatment

After describing the style characteristics of aircraft cabin decoration, the background greatly impacts aircraft cabin decoration, so it is necessary to preprocess the style background of aircraft cabin decoration. This study deals with the background of aircraft cabin decoration with the help of Gaussian mixture model. Gaussian mixture model method is a new background modeling method with strong background adaptability [10]. The basic idea is to model the pixel value of each pixel in several frames of background images using the Gaussian distribution function of probability statistics to obtain the multidimensional normal distribution of these pixels in time. In this algorithm, each pixel is analyzed through several Gaussian distributions, and the model can realize the preprocessing of complex environment background. In addition, the method shows strong adaptability to light changes. In the specific implementation, the method constructs K Gaussian distributions according to the value of each pixel in the multiframe image (i.e., the value set for a period). In order to accurately describe the distribution of the pixel value of the pixel in the scene over a period, the probability density function m weighted by the K Gaussian distributions is used to replace the fixed value of the pixel. Generally, the more the number of Gaussian distribution models, the more complex the background can be fitted by the model, but the computational time and cost also increase. The value of K can usually be selected according to the rate of background change. At the same time, it should take into account the calculation performance. The number of models is not the older the better. After the number of models is greater than a certain value, the algorithm may over fit the change of background, which will reduce the detection performance. Therefore, the value of K is generally between 3 and 7. Another advantage of Gaussian mixture model is that it contains background information and has a good detection effect on foreground interference [12]. The main reason is that some of the K Gaussian distribution models can describe the foreground of the law, which is equivalent to modeling the background and background at the same time, which is conducive to target detection in the complex foreground. The sampling result of a pixel in the T frame before time 1 is defined as follows:(3)Fa1;bm;cm;σm=∑m=1MbmNbm;cm;σm.Among them,Fa1;bm;cm;σm represents the number of Gaussian models, bm represents the probability density function of the M Gaussian model, cm represents the variance, and σm represents the weight coefficient. In the background of aircraft cabin decoration style, for the pixels to be measured, the Gaussian mixture model is processed by gradient updating [13] with the help of EM algorithm, and the following results are obtained:(4)bm←bm+ϑcm−bm,(5)cm←cm+ϑϑcmxnt,(6)σm←σm+cmxntσm.Among them,ϑ represents the learning rate and xnt represents the dependent parameters.On this basis, the input aircraft cabin decoration style sequence image is compared with the background image, the difference of statistical information such as gray characteristics of pixels or straight images is analyzed, the abnormal situation is judged, and the moving target is detected and recognized. Background subtraction generally includes three steps: (1) model initialization, modeling the background according to each pixel (input image). (2) Compare the current image with the background model and set the threshold. If the difference between the value of the image point in the current image (generally the gray value, or the combination of RGB channels) and the corresponding value of the background is greater than the threshold, the pixel point belongs to the foreground. Otherwise, it belongs to the background [14]. The final result of background preprocessing of aircraft cabin decoration style is as follows:(7)di,j,t=Ii,j,t−Bi,j,t,(8)di,j,t=1,di,j,t>TH,0,di,j,t>TH.In formula,di,j,t represents the result of the preprocessing, Ii,j,t represents the pixel point of the cabin differential aircraft image position, T represents the pixels of the current frame of the aircraft cabin image, and H represents the threshold [15] used by the binarization.In the background preprocessing of aircraft cabin decoration style, the background pixels of aircraft cabin decoration are determined with the help of Gaussian mixture model method, the style feature points to be measured are updated gradient, the input aircraft cabin decoration style sequence image is compared with the background image, and the differences of statistical information such as gray features of pixels or straight images are analyzed. Finally, the background preprocessing of aircraft cabin decoration style is completed.


## 2.1. Description of Characteristics of Aircraft Cabin Decoration Style

In order to improve the effect of aircraft cabin decoration style recommendation algorithm, firstly, the characteristics of aircraft cabin decoration style are described. In this study, the outline of aircraft cabin decoration style is described by HOG features [10], and the following results are obtained:(1)min∑p=1nt2AP−BPVP+μπ2.In the formula,AP represents the HOG characteristic matrix of the P perspective; BP and VP represent the base matrix and coefficient matrix of the P perspective, respectively; μ represents a smoothing factor, and P is the total number of perspectives.Based on this, clustering studies with the standardK-means clustering algorithm [11] obtained the IDF value of each cluster, i.e.,(2)IDFx=c∈E:C∈eN.In the formula,x represents a class cluster, N represents the total number of styles this class contains, N represents the total number of styles contains, and E represents the number of valid components that the style has. The aircraft cabin decoration style characteristics can be completed by selecting the appropriate IDF.In the description of cabin decoration style, the outline of HOG features andK-means clustering algorithm to complete the description.


## 2.2. Aircraft Cabin Trim Style Background Pretreatment

After describing the style characteristics of aircraft cabin decoration, the background greatly impacts aircraft cabin decoration, so it is necessary to preprocess the style background of aircraft cabin decoration. This study deals with the background of aircraft cabin decoration with the help of Gaussian mixture model. Gaussian mixture model method is a new background modeling method with strong background adaptability [10]. The basic idea is to model the pixel value of each pixel in several frames of background images using the Gaussian distribution function of probability statistics to obtain the multidimensional normal distribution of these pixels in time. In this algorithm, each pixel is analyzed through several Gaussian distributions, and the model can realize the preprocessing of complex environment background. In addition, the method shows strong adaptability to light changes. In the specific implementation, the method constructs K Gaussian distributions according to the value of each pixel in the multiframe image (i.e., the value set for a period). In order to accurately describe the distribution of the pixel value of the pixel in the scene over a period, the probability density function m weighted by the K Gaussian distributions is used to replace the fixed value of the pixel. Generally, the more the number of Gaussian distribution models, the more complex the background can be fitted by the model, but the computational time and cost also increase. The value of K can usually be selected according to the rate of background change. At the same time, it should take into account the calculation performance. The number of models is not the older the better. After the number of models is greater than a certain value, the algorithm may over fit the change of background, which will reduce the detection performance. Therefore, the value of K is generally between 3 and 7. Another advantage of Gaussian mixture model is that it contains background information and has a good detection effect on foreground interference [12]. The main reason is that some of the K Gaussian distribution models can describe the foreground of the law, which is equivalent to modeling the background and background at the same time, which is conducive to target detection in the complex foreground. The sampling result of a pixel in the T frame before time 1 is defined as follows:(3)Fa1;bm;cm;σm=∑m=1MbmNbm;cm;σm.Among them,Fa1;bm;cm;σm represents the number of Gaussian models, bm represents the probability density function of the M Gaussian model, cm represents the variance, and σm represents the weight coefficient. In the background of aircraft cabin decoration style, for the pixels to be measured, the Gaussian mixture model is processed by gradient updating [13] with the help of EM algorithm, and the following results are obtained:(4)bm←bm+ϑcm−bm,(5)cm←cm+ϑϑcmxnt,(6)σm←σm+cmxntσm.Among them,ϑ represents the learning rate and xnt represents the dependent parameters.On this basis, the input aircraft cabin decoration style sequence image is compared with the background image, the difference of statistical information such as gray characteristics of pixels or straight images is analyzed, the abnormal situation is judged, and the moving target is detected and recognized. Background subtraction generally includes three steps: (1) model initialization, modeling the background according to each pixel (input image). (2) Compare the current image with the background model and set the threshold. If the difference between the value of the image point in the current image (generally the gray value, or the combination of RGB channels) and the corresponding value of the background is greater than the threshold, the pixel point belongs to the foreground. Otherwise, it belongs to the background [14]. The final result of background preprocessing of aircraft cabin decoration style is as follows:(7)di,j,t=Ii,j,t−Bi,j,t,(8)di,j,t=1,di,j,t>TH,0,di,j,t>TH.In formula,di,j,t represents the result of the preprocessing, Ii,j,t represents the pixel point of the cabin differential aircraft image position, T represents the pixels of the current frame of the aircraft cabin image, and H represents the threshold [15] used by the binarization.In the background preprocessing of aircraft cabin decoration style, the background pixels of aircraft cabin decoration are determined with the help of Gaussian mixture model method, the style feature points to be measured are updated gradient, the input aircraft cabin decoration style sequence image is compared with the background image, and the differences of statistical information such as gray features of pixels or straight images are analyzed. Finally, the background preprocessing of aircraft cabin decoration style is completed.


## 3. Aircraft Cabin Decoration Style Recommendation Algorithms Based on Machine Vision

After the above aircraft cabin decoration style characteristics and background preprocessing, the machine vision method is used to recommend the aircraft cabin decoration style. Machine vision is a science and technology that uses computer to simulate biological macro vision function. Generally speaking, it is to use machines to replace human eyes for measurement and judgment [16]. Machine vision is characterized by automation, objectivity, noncontact, and high precision. Compared with image processing systems in the general sense, machine vision emphasizes precision, speed, and reliability in the industrial field environment. Typical machine vision components are shown in Figure 1:Figure 1
Typical machine vision composition.In machine vision, the array digital camera is used to obtain the visual signal, and the viewing and processing board integrated with image acquisition and processing is independently designed. The high-speed digital signal processor and Ethernet interface are used, which has very powerful image processing ability and convenient communication transmission function. The PC configures the required parameters of the processing card and obtains the color difference detection results through network communication. It realizes human-computer interaction control and other functions. Aiming at the problem that the resolution of a camera is limited and the width of dyed cloth is more than 1 m, we design a special camera motion control card to control the camera to move back and forth in the direction of cloth width, so as to realize image shooting in a wide range, which greatly saves the hardware cost of system design.In the 3D reconstruction method based on visual image, the camera model is pinhole camera model:(9)Qab1=frgb000frgb0001Ergbxyz1+yrgb.Among them,f is the camera focal length, Ergb and yrgb are the RGB camera posture, a, b is the pixel coordinate, and x, y, z is the object coordinate. Based on this camera model, multiple visual image camera posture can be restored through the beam method difference method.For the depth image, it can also be restored from the two-dimensional image to the three-dimensional model through a similar camera model. Different from the visual image camera model, the depth image can directly restore the object coordinates through the depth value, which can be expressed by formula (10):(10)EdepthUfdepthabfdepth+tdepth.Among them, theEdepth and fdepth are the deep camera attitude matrix obtained in the RGB-D SLAM method.As the initial value of beam adjustment, the depth camera attitude obtained by SLAM algorithm can solve the problem of scale recovery of single view camera on the one hand and improve the convergence of the algorithm on the other hand. Therefore, it is a necessary step to convert the depth camera attitude obtained by RGB-D slam to RGB camera model:(11)Uabfdepth=fdepthE−1xyz−Edepth−fdepth.After camera correction, the depth image and RGB image can correspond one by one pixel. Therefore, the camera attitude under the same camera model should be consistent. The relationship between the camera attitude under the depth image camera model and the camera attitude under the RGB image camera model can be obtained, as shown in the following formula:(12)Ergb=Edepth−1,trgb=−Edepth−1tdepth.After the aircraft cabin decoration is determined through machine vision, the obtained aircraft cabin decoration style will be effectively recommended, and the object of this style recommendation is the user. Therefore, it is necessary to obtain relevant data of users.SupposeIi=ei1,ei2,…eid for a 3D model of Class i, Where the e is the label of the style element, let si,j to indicate the number of times j class behavior for the i user.To facilitate statistics and analysis,si,j is normalized as follows:(13)Qij=si,j−minsi,jmaxsi,j−minsi,j.In the formula,maxsi,j and minsi,j represent the maximum and minimum number of j class behavior of the i user in a certain period. Set the weight to wj, and behavior j accounts for all behaviors of the user is PRij, the entropy of the behavior j is calculated as follows:(14)Ii=−∑i=1nlogPRij×Qij.In the formula,PRij indicates the number of times the i user appears behavior j, and n represents the total number of all behavior categories for the i user.Based on the weight entropy analysis, the style should be recommended by the current behavior information. Let thej class behavior i or time weight of the i user be Tij, and the final recommended result is given as:(15)Tij=elasti,j−firsti,jLi.In the formula, (i, j) indicates the time when the user i last produced the behavior j, Li represents the current time of the system, and first (i, j) indicates the time when the user i first produced the behavior j.In the research of aircraft cabin decoration style recommendation algorithm, the basic principle and operation process of machine vision system are analyzed, and the aircraft cabin decoration style image is obtained with the help of machine vision. On this basis, the weight value of recommended users is calculated, the number of decoration style recommendations is determined, and the aircraft cabin decoration style recommendation is completed.


## 4. Experimental Analysis

### 4.1. Aircraft Cabin Decoration Scene Setting

This experiment is the Boeing 737 aircraft cabin, as shown in Figure2:Figure 2
Sample aircraft cabin diagram.In this experiment, the plane cabin decoration scene is drawn in the indoor scene plan, as shown in Figure3:Figure 3
Interior scene plan of aircraft cabin decoration scene.On the basis of the above experimental scenes and plans, the comparison method, the indoor decoration color recommendation method based on genetic algorithm, and the three-dimensional indoor home-style recommendation method based on user interest model are used to recommend the decoration of the above sample aircraft cabin. Taking the precision of decoration style recommendation and the error of recommendation and decoration style feature extraction as the experimental indexes, the experimental analysis is carried out. The experimental data obtained in the experiment are processed by SPSS software, and the number of experimental iterations is 100. In order to ensure the accuracy of the experiment, the experimental data are calculated for many times.


### 4.2. Analysis of Experimental Results

The accuracy of aircraft cabin decoration style recommendation is the key index to measure the effectiveness of the recommendation algorithm. Therefore, the accuracy of sample decoration style recommendation by this method, interior decoration color recommendation method based on genetic algorithm, and three-dimensional interior home-style recommendation method based on user interest model are experimentally analyzed. Two groups of experiments are carried out, and the results are shown in Figure4:Figure 4
Recommended accuracy analysis of different methods.
(a)(b)By analyzing the trend of the two groups of experimental curves in Figure4, it can be seen that there are certain differences in the recommendation accuracy of the three methods in the two groups of recommendation experiments conducted under the same experimental environment. Among them, the recommended accuracy curve of this method shows a relatively stable change trend, whereas the recommended accuracy of the other two methods fluctuates greatly in the two groups of experiments. This is due to the continuous change in the amount of data in the decoration style recommendation. These two methods do not delete the background of the decoration feature data. In contrast, the recommended accuracy of this method is higher than 90%. This is because the method proposed in this study determines the background pixels of aircraft cabin decoration with the help of Gaussian mixture model method, updates the style feature points to be measured gradiently, compares the input aircraft cabin decoration style sequence image with the background image, analyzes the differences of statistical information such as gray features of pixels or straight images, and completes the background preprocessing of aircraft cabin decoration style. This study analyzes the basic principle and operation process of machine vision system and obtains the aircraft cabin decoration style image with the help of machine vision. Therefore, the effectiveness of the proposed method is improved.In order to further verify the effectiveness of this method, the errors of this method, the indoor decoration color recommendation method based on genetic algorithm, and the 3D indoor home-style recommendation method based on user interest model on the feature extraction of sample decoration style are experimentally analyzed. The experimental results are shown in Figure5:Figure 5
Error of different methods.By analyzing Figure5, it can be seen that there are certain differences in the error of sample decoration style feature extraction using this method, indoor decoration color recommendation method based on genetic algorithm, and 3D indoor home-style recommendation method based on user interest model. The extraction error of this method is always lower than that of the other two methods, and although the other two methods show a downward trend, the error difference is always higher than that of this method. This is because this method obtains the aircraft cabin decoration style image with the help of machine vision. On this basis, it calculates the weight value of recommended users and sorts the features according to the weight value. This method extracts the parts with high feature weight value, which improves the effectiveness of this method.


## 4.1. Aircraft Cabin Decoration Scene Setting

This experiment is the Boeing 737 aircraft cabin, as shown in Figure2:Figure 2
Sample aircraft cabin diagram.In this experiment, the plane cabin decoration scene is drawn in the indoor scene plan, as shown in Figure3:Figure 3
Interior scene plan of aircraft cabin decoration scene.On the basis of the above experimental scenes and plans, the comparison method, the indoor decoration color recommendation method based on genetic algorithm, and the three-dimensional indoor home-style recommendation method based on user interest model are used to recommend the decoration of the above sample aircraft cabin. Taking the precision of decoration style recommendation and the error of recommendation and decoration style feature extraction as the experimental indexes, the experimental analysis is carried out. The experimental data obtained in the experiment are processed by SPSS software, and the number of experimental iterations is 100. In order to ensure the accuracy of the experiment, the experimental data are calculated for many times.


## 4.2. Analysis of Experimental Results

The accuracy of aircraft cabin decoration style recommendation is the key index to measure the effectiveness of the recommendation algorithm. Therefore, the accuracy of sample decoration style recommendation by this method, interior decoration color recommendation method based on genetic algorithm, and three-dimensional interior home-style recommendation method based on user interest model are experimentally analyzed. Two groups of experiments are carried out, and the results are shown in Figure4:Figure 4
Recommended accuracy analysis of different methods.
(a)(b)By analyzing the trend of the two groups of experimental curves in Figure4, it can be seen that there are certain differences in the recommendation accuracy of the three methods in the two groups of recommendation experiments conducted under the same experimental environment. Among them, the recommended accuracy curve of this method shows a relatively stable change trend, whereas the recommended accuracy of the other two methods fluctuates greatly in the two groups of experiments. This is due to the continuous change in the amount of data in the decoration style recommendation. These two methods do not delete the background of the decoration feature data. In contrast, the recommended accuracy of this method is higher than 90%. This is because the method proposed in this study determines the background pixels of aircraft cabin decoration with the help of Gaussian mixture model method, updates the style feature points to be measured gradiently, compares the input aircraft cabin decoration style sequence image with the background image, analyzes the differences of statistical information such as gray features of pixels or straight images, and completes the background preprocessing of aircraft cabin decoration style. This study analyzes the basic principle and operation process of machine vision system and obtains the aircraft cabin decoration style image with the help of machine vision. Therefore, the effectiveness of the proposed method is improved.In order to further verify the effectiveness of this method, the errors of this method, the indoor decoration color recommendation method based on genetic algorithm, and the 3D indoor home-style recommendation method based on user interest model on the feature extraction of sample decoration style are experimentally analyzed. The experimental results are shown in Figure5:Figure 5
Error of different methods.By analyzing Figure5, it can be seen that there are certain differences in the error of sample decoration style feature extraction using this method, indoor decoration color recommendation method based on genetic algorithm, and 3D indoor home-style recommendation method based on user interest model. The extraction error of this method is always lower than that of the other two methods, and although the other two methods show a downward trend, the error difference is always higher than that of this method. This is because this method obtains the aircraft cabin decoration style image with the help of machine vision. On this basis, it calculates the weight value of recommended users and sorts the features according to the weight value. This method extracts the parts with high feature weight value, which improves the effectiveness of this method.


## 5. Conclusion

In order to improve the effect of aircraft cabin decoration style recommendation, this article describes the outline of aircraft cabin decoration style with ethe help of hog features and completes the description of aircraft cabin decoration style features by clustering observation throughK-means clustering algorithm. The background pixels of aircraft cabin decoration are determined with the help of Gaussian mixture model method, the style feature points to be measured are updated gradiently, the input aircraft cabin decoration style sequence image is compared with the background image, the difference of statistical information such as gray characteristics of pixels or straight image is analyzed, and the background preprocessing of aircraft cabin decoration style is completed. This study analyzed the basic principle and operation process of machine vision system, obtained the aircraft cabin decoration style image with the help of machine vision, calculated the weight value of recommended users, determined the number of decoration style recommendations, and completed the aircraft cabin decoration style recommendation. The experimental results show that this method has certain advantages. Although the method proposed in this study has achieved excellent performance, in the development of deep learning today, we should strive to integrate more advanced knowledge to achieve better performance.



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