# Image Composition and Semantic Expression of Traditional Folk Art Based on Evolutionary Computing Technology

Authors: Suili Ma
Journal: Mobile Information Systems
 (2022)

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## Abstract

Image media are used by people to perceive the world’s material reality and spiritual symbols. Traditional folk art images, unlike natural scene images, are characterized by “form to write God.” Their semantic data are more abstract and detailed. As a result, folk art images limit the use of low-level visual feature descriptors in natural images. By simulating the evolution of natural species, evolutionary computation solves optimization problems. Black box optimization, combinatorial optimization, nonconvex optimization, and multiobjective optimization are all examples of optimization techniques. The semantic model is built using the semantic dictionary, and the semantic expression between images is then determined using semantic measurement. As a result, this paper focuses on the “semantic gap” of evolutionary computing-based image search technology, as well as related feedback and image semantic analysis technology. The results show that this method uses the deep database’s triple cross validation method to achieve average comfort and wakefulness, with accuracy of 63.62 percent and 72.38 percent, respectively, which is better than the methods in the comparative literature and can verify the algorithm’s efficiency and feasibility. To summarize, using evolutionary computing technology to improve the performance of object classification and retrieval, traditional folk art image composition and semantic expression can effectively reduce quantization error and improve the resolution of image semantic expression.


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## Body

## 1. Introduction

Traditional folk art is an essential component of Chinese civilization and culture. It comes from Chinese children’s labor practices and daily lives, inheriting the sweat and wisdom of forefathers and blending into the blood of the national spirit [1]. Folk paintings are unique in that they exist as traditional folk culture while still retaining their distinctive characteristics [2]. For contemporary Chinese visual design, primitive sensibility, chaotic artistic expression, modeling ceremony, primitive religious concept, yin and yang, and five-element philosophy, profound national cultural connotation and localized narrative style provide inspiration and innovation [3]. However, as modern society has evolved, rapid changes in multidimensional social life concepts, life views, and values, such as the diversity and richness of life, have also resulted from the stereotype, stylization, unity, and stability of folk art, as well as the conflict between contemporary and traditional artistic concepts, putting these ancient and traditional folk crafts to the test.Images are typically manually marked with text in the early stages of image segmentation [4–6], classification [7–9], and other tasks [10–12] and then processed using text analysis technology, such as text-based image search. Existing convex optimization algorithms struggle to solve these practical optimization problems, so computing has emerged as the preferred method. Folk art has a wide range of potential applications in education, commerce, Internet search, and other fields because it has a broad range of application prospects. The basic visual features of an image, such as color, texture, and shape, are primarily used by evolution to describe the content of the image, and the distance between these features is used to judge the image’s similarity. Manual recognition of images is performed. To put it in another way, annotate the image with keywords or text words, then search for them. To avoid excessive information loss, the filtered features must be redundant and independent of the classification label, and the remaining feature subset must outperform the original feature set.How to effectively express and measure the meaning of images is the most difficult problem to solve in order to complete tasks like image annotation, search, and classification accurately and efficiently. If the semantic categories of paintings can be automatically analyzed, automatic classification of paintings can be realized, and the artist’s semantic annotation can be done on this basis, which will be a very useful auxiliary tool in the creation of digital paintings. Shapes and shape combinations produce semantics [13]. Semantic patterns are, simply put, patterns and mechanisms that connect form and meaning. Digital image processing, multimedia information retrieval, machine learning and deep learning technology [14], multimedia database management, and many other related technologies [15] are all used in the image composition and semantic expression of folk art using evolutionary computing technology. This is an interdisciplinary subject, and the related research is crucial to image processing technology, signal processing technology, and information processing technology theory. The innovation of this paper lies in the following:(1)
The image composition and semantic expression based on evolutionary computing technology proposed in this paper can be applied to neural network optimization, that is, to study the domain of evolutionary neural network and to optimize the network topology, the training hyperparameters of the network, the connection weights of the neural network, and so forth.(2)
Different from the traditional neural network training methods, this paper equates the training problem of neural networks with different structures to a multidimensional multitask optimization problem and uses the heterogeneous multidimensional multitask neural evolutionary algorithms to train all networks at the same time.


## 2. Related Work

### 2.1. Image Composition and Semantic Expression of Traditional Folk Art

Because there is usually no direct connection between low-level visual features of images and high-level semantic concepts, automatic semantic classification and annotation of painting images is a difficult research topic. According to Lu et al.’s research, the method based on evolutionary computation has a stronger global searching ability, making it a more efficient and practical searching method that is also widely used in image composition analysis [16]. The current image features cannot accurately correspond to the semantic content of the image due to the semantic gap. Even the appearance of a series of image representation models, such as the visual word bag model, visual language model, and learning coding model, is only attempting to narrow the semantic gap by building the image’s middle-level semantic concept model. Liu and Wang pushed iconology research into a broader context. They incorporated previous theoretical achievements into their representative work “The Meaning of Visual Art” and formally proposed three levels of iconology research [17]. Tang and Liu used powerful computers and human vision to bring search results closer to human vision [18]. As a result, using the distance between image features to measure the difference between image meanings is both unreasonable and inaccurate. The image’s distance is calculated from the semantic distance. Shang used a genetic algorithm to optimize the parameters of a support vector machine, demonstrating that a genetic algorithm can achieve both parameter optimization and feature subset selection without compromising classification performance [19]. The main idea behind supervised distance metric learning is to learn a metric matrix with premarked data and then map and transform the samples to minimize the distance between semantically similar samples in the transformed metric space, while separating semantically irrelevant samples and increasing the distance between them. In terms of image composition, Lu proposed using supervised distance measurement learning to measure the sparsity and granularity of image edges [20], based on the above-mentioned characteristics of meticulous painting and freehand painting.


### 2.2. Multitask Evolutionary Computation

The research on the automatic classification and marking of digital images in folk art includes the integration of computer vision, machine learning, image search, cognitive psychology, and painting art. Multitask evolutionary computation refers to using evolutionary algorithm to optimize several similar tasks at the same time. Its pioneering algorithm is multifactor evolutionary algorithm. Folk art focuses on object recognition technology, content-based image retrieval technology, and the image nature of “depicting ideas in form” to create low-level visual feature predicates in computer vision. Some restrictions on images in folk art (e.g., color, shape, texture, etc.) apply to images. Jo proposed the concept of multitask computing in 2016 and proposed the first multitask optimization algorithm (MFEA) [21]. Different from the accurate semantic information expressed by general digital images, one feature of digital images is that the semantic information reflected and expressed is abstract. MFEA is a new algorithm, which uses a single population to solve multiple optimization problems at the same time and maps the optimization space of different tasks to the standardized optimization interval. Mimicry allows genes to pass on to each other in different tasks. Dlb et al. proposed a feature weighted filtering feature selection algorithm (relevant features, relief), which assigns different weights to features for feature selection according to the relevant information of each feature and category [22]. Guo et al. first proposed the multiobjective and multitask evolutionary algorithm (MO-MFEA) and verified the efficiency of the algorithm in practical multiobjective optimization problems [23]. Halstead et al. proposed an algorithm based on greedy search, which continuously optimizes the performance of the classification learner by sequentially selecting a feature from the remaining feature set and adding it to the selected feature subset [24]. Markey et al. extended the concept of multitask evolutionary computing to genetic programming (GP) and proposed multifactor genetic programming [25].


## 2.1. Image Composition and Semantic Expression of Traditional Folk Art

Because there is usually no direct connection between low-level visual features of images and high-level semantic concepts, automatic semantic classification and annotation of painting images is a difficult research topic. According to Lu et al.’s research, the method based on evolutionary computation has a stronger global searching ability, making it a more efficient and practical searching method that is also widely used in image composition analysis [16]. The current image features cannot accurately correspond to the semantic content of the image due to the semantic gap. Even the appearance of a series of image representation models, such as the visual word bag model, visual language model, and learning coding model, is only attempting to narrow the semantic gap by building the image’s middle-level semantic concept model. Liu and Wang pushed iconology research into a broader context. They incorporated previous theoretical achievements into their representative work “The Meaning of Visual Art” and formally proposed three levels of iconology research [17]. Tang and Liu used powerful computers and human vision to bring search results closer to human vision [18]. As a result, using the distance between image features to measure the difference between image meanings is both unreasonable and inaccurate. The image’s distance is calculated from the semantic distance. Shang used a genetic algorithm to optimize the parameters of a support vector machine, demonstrating that a genetic algorithm can achieve both parameter optimization and feature subset selection without compromising classification performance [19]. The main idea behind supervised distance metric learning is to learn a metric matrix with premarked data and then map and transform the samples to minimize the distance between semantically similar samples in the transformed metric space, while separating semantically irrelevant samples and increasing the distance between them. In terms of image composition, Lu proposed using supervised distance measurement learning to measure the sparsity and granularity of image edges [20], based on the above-mentioned characteristics of meticulous painting and freehand painting.


## 2.2. Multitask Evolutionary Computation

The research on the automatic classification and marking of digital images in folk art includes the integration of computer vision, machine learning, image search, cognitive psychology, and painting art. Multitask evolutionary computation refers to using evolutionary algorithm to optimize several similar tasks at the same time. Its pioneering algorithm is multifactor evolutionary algorithm. Folk art focuses on object recognition technology, content-based image retrieval technology, and the image nature of “depicting ideas in form” to create low-level visual feature predicates in computer vision. Some restrictions on images in folk art (e.g., color, shape, texture, etc.) apply to images. Jo proposed the concept of multitask computing in 2016 and proposed the first multitask optimization algorithm (MFEA) [21]. Different from the accurate semantic information expressed by general digital images, one feature of digital images is that the semantic information reflected and expressed is abstract. MFEA is a new algorithm, which uses a single population to solve multiple optimization problems at the same time and maps the optimization space of different tasks to the standardized optimization interval. Mimicry allows genes to pass on to each other in different tasks. Dlb et al. proposed a feature weighted filtering feature selection algorithm (relevant features, relief), which assigns different weights to features for feature selection according to the relevant information of each feature and category [22]. Guo et al. first proposed the multiobjective and multitask evolutionary algorithm (MO-MFEA) and verified the efficiency of the algorithm in practical multiobjective optimization problems [23]. Halstead et al. proposed an algorithm based on greedy search, which continuously optimizes the performance of the classification learner by sequentially selecting a feature from the remaining feature set and adding it to the selected feature subset [24]. Markey et al. extended the concept of multitask evolutionary computing to genetic programming (GP) and proposed multifactor genetic programming [25].


## 3. Image Feature Extraction and Distance Measurement Method

### 3.1. Image Feature Extraction Method

Image feature extraction mainly refers to the use of specific operators to extract vectors representing image information [26]. Once the image is born, it naturally has the function of information transmission and expression and becomes “text” and medium [27]. The cultural psychology of a nation is adapted to the social and cultural background of the times when it came into being and existed [28]. The main process of image feature extraction is shown in Figure 1.Figure 1
Flow chart of image feature extraction method.Firstly, the global features of the image mainly include color features, texture features, and shape features, which can be regarded as the result of the action of operatorL on the global image I. The purpose is to find the place where the brightness changes rapidly in the image and detect the discontinuity of brightness value. Expand the brightness value twice:(1)Dx=D+∂DT∂xx+12xT∂2x2.In the step of extracting the low-level visual features representing the semantics of image scenes, according to whether the mapping relationship is established between the low-level visual features of images and the high-level semantic objects, we roughly divide the existing image scene classification algorithms into two categories, namely, the low-level visual features of images and the middle-level semantic representation mechanism based on images. Taking pointO as the origin of the coordinate system, the length of the line segment from any point x,y on the template to origin O is D, and the angle with the positive half axis of x is θ; then(2)D=x2+y2,θ=arctanyx.Color function is one of the earliest functions used to represent image content, which usually depends on color space models such as GB, QE, and HSV [29]. However, because the distribution characteristics of color information in two-dimensional space are not considered, images with different contents may have similar color histograms. If only blue and other color features are used to process the image, misclassification can be easily caused. The sky and the blue sea easily fall into the same category. Therefore, the global image function is more suitable for processing images with obvious regional edges. Using the edge detection function and the internal structure of the image, the complexity index of the internal structure of the image describes the complexity SC of the internal structure, and its calculation formula is(3)SC=EdgeDia.Secondly, the computational complexity of the global features of the image is small, and it is insensitive to the change of viewing angle. However, they can only reflect the global statistical information of the image, ignoring the local details. The edge response of the image can be modeled by Weibull distribution. The formula of Weibull distribution is as follows:(4)fx=γβγβγ−1e−x/βγ.Using image segmentation technology, the image is divided into several homogeneous regions, and then the semantic object category of each homogeneous region is given in a fully automatic or semiautomatic way. Then, training samples are collected for each semantic object, and a supervised learning mechanism is used to construct a classifier. Through the comparison with the threshold, the pixels in the image are classified. For the pixel pointx,y, which is the edge in the edge image, the derivative in the grayscale image is calculated, and the edge direction ϕx,y of pixel point x,y is calculated:(5)ϕx,y=acosdG/dxdG/dx2+dG/dx2×180∘.The image is divided into uniform grids, and local feature areas of the same size are obtained, followed by the description of the local feature areas. Because normal mesh extraction covers the entire image, it can make the best use of image data, but it also introduces a slew of feature issues, and reckless interpretation invariably introduces a lot of redundant data. A regular grid can also be used to highlight important parts of the image and introducing a visual attention mechanism can effectively extract and describe the areas in the rendered image which best represent semantic information.


### 3.2. Image Distance Measurement Method

The essence of image distance measurement is the correlation research based on circular image semantic table method, including the correlation between image underlying features, the correlation between visual words, and the correlation between image semantic representation vectors. Euclidean distance is one of the most understandable distance calculation methods. It comes from the formula of the distance between two points in Euclidean space. Its calculation method is(6)Dx,y=∑i=1dxi−yi2.Manhattan distance is calculated as follows:(7)DX,Y=∑i=1dxi−yiThe cosine distance is calculated as follows:(8)DX,Y=1−∑i=1dxiyi∑i=1dxi2∑i=1dyi2.Relevance is to filter features according to specific criteria, regardless of learners’ performance. In other words, the feature selection process is therefore effective because it first completes feature selection and then trains the learner with the selected features. The flow of image distance measurement is shown in Figure2.Figure 2
Image distance measurement.First, when the signal of interest is compressible or sparse, the signal can be accurately obtained with few samples. Therefore, after the semantic representation vector of images is constructed, it is necessary to measure the similarity of them and the similarity between images. According to certain standards, by growing small areas, merging them into large areas, or dividing large areas into small areas, we can find and measure feature subsets in the image.K−L divergence is often used to measure the asymmetric difference between two probability distributions. Assuming that the two probability distributions are P=p1,p2,...,pn,Q=q1,q2,...,.qn, the K−L divergence between them is(9)DPQ=∑i=1npilogpiqi.Because a specific evaluation function determines whether a feature subset is superior or not, defining a different evaluation function will have an impact on the feature subset’s selection result [30]. A seed pixel is chosen, and a growth criterion is specified, indicating that pixels with similar characteristics to the seed pixel can be merged, and the pixels or regions that meet the criterion are merged step by step. The feature subset generation process can use a specific search strategy to generate candidate feature subsets and evaluate them, starting with the original feature set as a starting point. Each image’s depth learning features are solved using a standard gradient optimization algorithm, and the depth learning features are fused to produce the image’s depth learning representation vector. Figure 3 illustrates the flow of the image bifurcation expression method.Figure 3
Image semantic expression method flow.Secondly, an important method to solve the image distance measurement problem is to construct an auxiliary function, so that the unconstrained minimum of this function is also the minimum of the constrained problem. Then, the unconstrained optimization minimization method is used to find the minimum of the auxiliary function, so as to obtain the optimal solution of the original problem. Assuming thatIx,y is the original image and Lx,y,σ is the transformed image, the unconstrained optimization minimization method is used for calculation:(10)Lx,y,σ=Gx,y,σ∗Ix,y.The kernel norm is equal to the singular value vector of the matrix’sl norm and the rank corresponding vector’s zero norm. The kernel norm differs from the zero norm in that it is convex, whereas the zero norm is not a mathematical norm. However, the goal of distance metric learning is to obtain a transformation from training data that can reflect the structural information or semantic constraint information of the sample space, as well as a more distinguishable metric space, so that the matching between feature space and semantic space is not visible. Unsupervised distance learning and supervised distance learning are the two types of distance learning methods. The stop condition determines whether the feature selection process ends or not, preventing the search process from looping indefinitely. In general, the stopping condition can be the algorithm’s maximum iteration times, the feature set threshold, the classification accuracy threshold, the maximum execution time, and so on. Because the influence of background has a high global anomaly in complex scenes, these methods are of great importance, and the results are different.Throughout history, traditional folk art has played various roles. It reflects the Chinese working class’s global outlook and values, as well as the Chinese nation’s unique cultural and psychological structure, aesthetic taste, and artistic ideal. In order to fully display the aesthetic interest of folk art graphics, relevant personnel must design folk art graphics in conjunction with specific occasions and places during the image distance measurement process.


## 3.1. Image Feature Extraction Method

Image feature extraction mainly refers to the use of specific operators to extract vectors representing image information [26]. Once the image is born, it naturally has the function of information transmission and expression and becomes “text” and medium [27]. The cultural psychology of a nation is adapted to the social and cultural background of the times when it came into being and existed [28]. The main process of image feature extraction is shown in Figure 1.Figure 1
Flow chart of image feature extraction method.Firstly, the global features of the image mainly include color features, texture features, and shape features, which can be regarded as the result of the action of operatorL on the global image I. The purpose is to find the place where the brightness changes rapidly in the image and detect the discontinuity of brightness value. Expand the brightness value twice:(1)Dx=D+∂DT∂xx+12xT∂2x2.In the step of extracting the low-level visual features representing the semantics of image scenes, according to whether the mapping relationship is established between the low-level visual features of images and the high-level semantic objects, we roughly divide the existing image scene classification algorithms into two categories, namely, the low-level visual features of images and the middle-level semantic representation mechanism based on images. Taking pointO as the origin of the coordinate system, the length of the line segment from any point x,y on the template to origin O is D, and the angle with the positive half axis of x is θ; then(2)D=x2+y2,θ=arctanyx.Color function is one of the earliest functions used to represent image content, which usually depends on color space models such as GB, QE, and HSV [29]. However, because the distribution characteristics of color information in two-dimensional space are not considered, images with different contents may have similar color histograms. If only blue and other color features are used to process the image, misclassification can be easily caused. The sky and the blue sea easily fall into the same category. Therefore, the global image function is more suitable for processing images with obvious regional edges. Using the edge detection function and the internal structure of the image, the complexity index of the internal structure of the image describes the complexity SC of the internal structure, and its calculation formula is(3)SC=EdgeDia.Secondly, the computational complexity of the global features of the image is small, and it is insensitive to the change of viewing angle. However, they can only reflect the global statistical information of the image, ignoring the local details. The edge response of the image can be modeled by Weibull distribution. The formula of Weibull distribution is as follows:(4)fx=γβγβγ−1e−x/βγ.Using image segmentation technology, the image is divided into several homogeneous regions, and then the semantic object category of each homogeneous region is given in a fully automatic or semiautomatic way. Then, training samples are collected for each semantic object, and a supervised learning mechanism is used to construct a classifier. Through the comparison with the threshold, the pixels in the image are classified. For the pixel pointx,y, which is the edge in the edge image, the derivative in the grayscale image is calculated, and the edge direction ϕx,y of pixel point x,y is calculated:(5)ϕx,y=acosdG/dxdG/dx2+dG/dx2×180∘.The image is divided into uniform grids, and local feature areas of the same size are obtained, followed by the description of the local feature areas. Because normal mesh extraction covers the entire image, it can make the best use of image data, but it also introduces a slew of feature issues, and reckless interpretation invariably introduces a lot of redundant data. A regular grid can also be used to highlight important parts of the image and introducing a visual attention mechanism can effectively extract and describe the areas in the rendered image which best represent semantic information.


## 3.2. Image Distance Measurement Method

The essence of image distance measurement is the correlation research based on circular image semantic table method, including the correlation between image underlying features, the correlation between visual words, and the correlation between image semantic representation vectors. Euclidean distance is one of the most understandable distance calculation methods. It comes from the formula of the distance between two points in Euclidean space. Its calculation method is(6)Dx,y=∑i=1dxi−yi2.Manhattan distance is calculated as follows:(7)DX,Y=∑i=1dxi−yiThe cosine distance is calculated as follows:(8)DX,Y=1−∑i=1dxiyi∑i=1dxi2∑i=1dyi2.Relevance is to filter features according to specific criteria, regardless of learners’ performance. In other words, the feature selection process is therefore effective because it first completes feature selection and then trains the learner with the selected features. The flow of image distance measurement is shown in Figure2.Figure 2
Image distance measurement.First, when the signal of interest is compressible or sparse, the signal can be accurately obtained with few samples. Therefore, after the semantic representation vector of images is constructed, it is necessary to measure the similarity of them and the similarity between images. According to certain standards, by growing small areas, merging them into large areas, or dividing large areas into small areas, we can find and measure feature subsets in the image.K−L divergence is often used to measure the asymmetric difference between two probability distributions. Assuming that the two probability distributions are P=p1,p2,...,pn,Q=q1,q2,...,.qn, the K−L divergence between them is(9)DPQ=∑i=1npilogpiqi.Because a specific evaluation function determines whether a feature subset is superior or not, defining a different evaluation function will have an impact on the feature subset’s selection result [30]. A seed pixel is chosen, and a growth criterion is specified, indicating that pixels with similar characteristics to the seed pixel can be merged, and the pixels or regions that meet the criterion are merged step by step. The feature subset generation process can use a specific search strategy to generate candidate feature subsets and evaluate them, starting with the original feature set as a starting point. Each image’s depth learning features are solved using a standard gradient optimization algorithm, and the depth learning features are fused to produce the image’s depth learning representation vector. Figure 3 illustrates the flow of the image bifurcation expression method.Figure 3
Image semantic expression method flow.Secondly, an important method to solve the image distance measurement problem is to construct an auxiliary function, so that the unconstrained minimum of this function is also the minimum of the constrained problem. Then, the unconstrained optimization minimization method is used to find the minimum of the auxiliary function, so as to obtain the optimal solution of the original problem. Assuming thatIx,y is the original image and Lx,y,σ is the transformed image, the unconstrained optimization minimization method is used for calculation:(10)Lx,y,σ=Gx,y,σ∗Ix,y.The kernel norm is equal to the singular value vector of the matrix’sl norm and the rank corresponding vector’s zero norm. The kernel norm differs from the zero norm in that it is convex, whereas the zero norm is not a mathematical norm. However, the goal of distance metric learning is to obtain a transformation from training data that can reflect the structural information or semantic constraint information of the sample space, as well as a more distinguishable metric space, so that the matching between feature space and semantic space is not visible. Unsupervised distance learning and supervised distance learning are the two types of distance learning methods. The stop condition determines whether the feature selection process ends or not, preventing the search process from looping indefinitely. In general, the stopping condition can be the algorithm’s maximum iteration times, the feature set threshold, the classification accuracy threshold, the maximum execution time, and so on. Because the influence of background has a high global anomaly in complex scenes, these methods are of great importance, and the results are different.Throughout history, traditional folk art has played various roles. It reflects the Chinese working class’s global outlook and values, as well as the Chinese nation’s unique cultural and psychological structure, aesthetic taste, and artistic ideal. In order to fully display the aesthetic interest of folk art graphics, relevant personnel must design folk art graphics in conjunction with specific occasions and places during the image distance measurement process.


## 4. Analysis of Heterogeneous Multifactor Evolutionary Algorithm and Neural Evolutionary Algorithm

### 4.1. Analysis of Heterogeneous Multifactor Evolutionary Algorithm

MFEA is the first multitask evolutionary algorithm that can simultaneously optimize multiple problems in a single population. That is to say, using population exploration technology, the population is represented as a set of problem solutions, and a set of genetic operations such as selection, crossover, and mutation are carried out on the current population to generate a new generation of population, which will be in the majority state of the optimal solution. It gradually evolved to include the traditional single-objective genetic algorithm using real or binary coding. The main process of the algorithm is to generate offspring through cross mutation and then select good individuals to pass on to the next generation through selection operation. Different from traditional methods, multifactor evolutionary algorithm takes the performance of learners as the criterion for evaluating feature subsets. That is to say, it directly uses the selected feature subset to train learners and then evaluates the performance of features according to the classification effect. SVM classifier completes target classification by learning in traditional Euclidean space and new metric space and verifies the influence of image feature groups on target classification accuracy. The results are shown in Figure4.Figure 4
Influence of the number of feature groups on the classification error rate of eye trace.Firstly, MFEA uses a single population to optimize all tasks at the same time, but the search space of different tasks is different and the dimensions may be different. It is necessary to design a new coding method to map a single individual to multiple tasks. According to MFEA, individualpa is encoded as a vector with dimension Dm. When decoding, MFEA only decodes the first Dτi genes of pa. The chi square model is used to remove a certain number of visual stop words, verify the impact of removing different numbers of visual stop words on the target retrieval results, and perform comparison with the target retrieval results without visual stop word removal, and the retrieval map value is shown in Figure 5.Figure 5
Effect of removing the number of stop words on the map value of target retrieval.In feature coding, the probability of each feature being selected can be coded into a group ofN-ary digit strings with length L, that is, the coding process. The heterogeneous multifactor, evolutionary algorithm proposed in this chapter can get the best classification results with less learning time. After the introduction of feature grouping algorithm, the learning time can be further reduced. Therefore, the method in this chapter still has strong practicability in large-scale data. The classification AP and learning time of different distance measurement learning methods are shown in Table 1.Table 1
Classification AP and learning time of different distance measurement learning methods.
MethodTarget classification AP (%)Learning time (s)LMNN73.21876ITML83.14930NCA74.94301Different multidimensional factor evolutionary algorithm83.66872The sum of squares of element values in each column of the matrix corresponds to a corresponding image block, so the more prominent the image block is, the larger the sum of squares of element values in the column is. In the same way, the lower the value is, the less noticeable the block is. Set merging and segmentation criteria and then isolate small areas or pixels with low similarity and merge areas or pixels with high similarity until the segmentation is complete. Some constraint rules are added to the learning algorithm’s model training process to simplify features. The corresponding feature subset can be obtained after the model training is completed. Even if the tasks are very similar, gene transfer is unlikely to play a role because offspring have a very low standard fitness value and are likely to be eliminated in iteration due to harmful transfer.Secondly,Dτi≠Dτb,rand<rmp: For the convenience of discussion, let us assume that the offspring generated by the crossover of Dτa<Dτb, pa and pb, randomly inherit the optimal factor of the parent and complete the gene migration in different tasks. In order to accurately locate the position and scale of key points and remove points with low contrast and key points with unstable edge response, classification experiments are carried out on CALTECH-256 dataset, respectively, and the performance of this chapter’s method is verified by comparing it with other classical target classification methods, including methods based on traditional visual word bag model and sparse coding model. The AP classification values are shown in Table 2.Table 2
Classification AP values.
Target classification methodVisual dictionary scaleCALTECH-256SA80069.4ScSPM80067.9LLC100078.4Method of this chapter150089.4Take an 8 × 8 window centered on the key point. Each cell represents a pixel near the key point, the direction of the arrow represents the direction of the gradient in the pixel, and the length of the arrow represents the gradient. We reevaluate the performance by inverting the hexagram segments of the digital string, so that the current hexagram can choose the appropriate direction, so as to find a better solution in each iteration. The points in the saliency map have topological correspondence with the pixels in the input image. Different visual features (color, texture, direction, etc.) can be considered to have different contributions to saliency. Use visual features as vertical and horizontal vectors to create two-dimensional filter templates, also known as filters. For all benchmark problems, MFEA is much smaller than other algorithms in mean and variance, which shows that MFEA has strong advantages in solution accuracy and stability.


### 4.2. Analysis of Neural Evolution Algorithm with Different Multidimensional Factors

We use the single hidden layer neural network to fit the chaotic time series. Although the single hidden layer neural network has strong nonlinear fitting ability, it is easy to fall into the local optimum by using parameterλ. In general, the choice of this parameter depends on the experiments on salient object fields. The smaller λ is, the more parameter blocks may be considered as significant. Ashmolean_3 and Christchurch_5 in Oxford SK dataset are the query targets, and the hard allocation method (AKM + HA) is compared with the soft allocation method (AKM + SA), and the recall rate is obtained. The precision curve is shown in Figure 6.Figure 6
Accuracy curve.Because it is different from the exact semantic information expressed by ordinary natural scenery digital images, the characteristic of folk art digital images is that the semantic information it reflects and expresses is abstract. The template is convolved with the image. After convolution filtering, the image changes in the vertical and horizontal directions, and two hidden layer nodes are obtained. Different hidden layer nodes mean different task dimensions. The more hidden layer nodes, the higher the dimension of the task. Because of the dimension disaster, it becomes more difficult to optimize tasks in high-dimensional space, so we need to quickly transfer the information from low-dimensional networks to high-dimensional networks. In common search algorithms, the expression of the solution can take any form, generally without special treatment. But, in evolutionary computation, every solution of the original problem is regarded as a biological individual. The visual language model (VLM) is used to classify 10 target categories in CALTECH-256 image set in these visual dictionaries. The AP value of the target classification changes with the size of the visual dictionary as shown in Figure7.Figure 7
Effect of parameterk on the number of hash buckets and target classification accuracy.To begin, we choose three benchmark datasets from which to predict the mixed pure time series: Lorenz, Mackey glass, and sunspots. Computer simulations were used to create the Lorenz and Mackey glass datasets. The dataset is a real-world dataset that is relatively complex. The key points in the training image are detected using the dog operator, and the SIFT feature descriptor is obtained. The input folk art images are divided into regular image subregions using a simple spatial grid layout Image scale invariant attribute transformation descriptor based on the characteristics of folk art images. The feature clustering result obtained by combining feature points with pixels corresponds to a pixel region of the image in the clustering analysis of image features. The image extraction descriptor is realized by mapping clusters to pixel sets. For 10 different types of CALTECH-256 image sets, compare the time consumption of different dimensional multifactor neural evolutionary algorithms and AKM algorithms in constructing visual dictionaries. The experimental results are shown in Figure8.Figure 8
Comparison of time efficiency of different algorithms.Secondly, the fourth-order Runge-Kutta method is used to solve this differential equation, the integration time step is 0.01, the initial value of the equation is a random number between 0 and 5, and the first 30000 points are omitted to ensure the stability of the sequence. Assuming that the key points with similar spatial positions are similar, we can tie them together, calculate their quality, and regard them as a representative key point. Then, images with low entropy often contain a core object (i.e., significant areas are more obvious), while images with high entropy contain multiple objects under different textures (significant areas are not obvious or multiple areas attract attention). On the basis of each kind of mean value, the pixels close to each kind of mean value are classified into corresponding classes according to the principle of distance. All sample data are projected onto multiple models, respectively, and the probability of samples in corresponding categories is obtained. All sample data are projected on several models, respectively, and the probability of samples in the corresponding classes is obtained. The set of contemporary initial solutions and their transformed solutions is called candidate solution pool, which is labeled asHmap. Therefore, for a current state Sn, the candidate solution pool is the set of the current hexagram state and its state transformed by interleaving operator, inversion operator, and mutual operator.


## 4.1. Analysis of Heterogeneous Multifactor Evolutionary Algorithm

MFEA is the first multitask evolutionary algorithm that can simultaneously optimize multiple problems in a single population. That is to say, using population exploration technology, the population is represented as a set of problem solutions, and a set of genetic operations such as selection, crossover, and mutation are carried out on the current population to generate a new generation of population, which will be in the majority state of the optimal solution. It gradually evolved to include the traditional single-objective genetic algorithm using real or binary coding. The main process of the algorithm is to generate offspring through cross mutation and then select good individuals to pass on to the next generation through selection operation. Different from traditional methods, multifactor evolutionary algorithm takes the performance of learners as the criterion for evaluating feature subsets. That is to say, it directly uses the selected feature subset to train learners and then evaluates the performance of features according to the classification effect. SVM classifier completes target classification by learning in traditional Euclidean space and new metric space and verifies the influence of image feature groups on target classification accuracy. The results are shown in Figure4.Figure 4
Influence of the number of feature groups on the classification error rate of eye trace.Firstly, MFEA uses a single population to optimize all tasks at the same time, but the search space of different tasks is different and the dimensions may be different. It is necessary to design a new coding method to map a single individual to multiple tasks. According to MFEA, individualpa is encoded as a vector with dimension Dm. When decoding, MFEA only decodes the first Dτi genes of pa. The chi square model is used to remove a certain number of visual stop words, verify the impact of removing different numbers of visual stop words on the target retrieval results, and perform comparison with the target retrieval results without visual stop word removal, and the retrieval map value is shown in Figure 5.Figure 5
Effect of removing the number of stop words on the map value of target retrieval.In feature coding, the probability of each feature being selected can be coded into a group ofN-ary digit strings with length L, that is, the coding process. The heterogeneous multifactor, evolutionary algorithm proposed in this chapter can get the best classification results with less learning time. After the introduction of feature grouping algorithm, the learning time can be further reduced. Therefore, the method in this chapter still has strong practicability in large-scale data. The classification AP and learning time of different distance measurement learning methods are shown in Table 1.Table 1
Classification AP and learning time of different distance measurement learning methods.
MethodTarget classification AP (%)Learning time (s)LMNN73.21876ITML83.14930NCA74.94301Different multidimensional factor evolutionary algorithm83.66872The sum of squares of element values in each column of the matrix corresponds to a corresponding image block, so the more prominent the image block is, the larger the sum of squares of element values in the column is. In the same way, the lower the value is, the less noticeable the block is. Set merging and segmentation criteria and then isolate small areas or pixels with low similarity and merge areas or pixels with high similarity until the segmentation is complete. Some constraint rules are added to the learning algorithm’s model training process to simplify features. The corresponding feature subset can be obtained after the model training is completed. Even if the tasks are very similar, gene transfer is unlikely to play a role because offspring have a very low standard fitness value and are likely to be eliminated in iteration due to harmful transfer.Secondly,Dτi≠Dτb,rand<rmp: For the convenience of discussion, let us assume that the offspring generated by the crossover of Dτa<Dτb, pa and pb, randomly inherit the optimal factor of the parent and complete the gene migration in different tasks. In order to accurately locate the position and scale of key points and remove points with low contrast and key points with unstable edge response, classification experiments are carried out on CALTECH-256 dataset, respectively, and the performance of this chapter’s method is verified by comparing it with other classical target classification methods, including methods based on traditional visual word bag model and sparse coding model. The AP classification values are shown in Table 2.Table 2
Classification AP values.
Target classification methodVisual dictionary scaleCALTECH-256SA80069.4ScSPM80067.9LLC100078.4Method of this chapter150089.4Take an 8 × 8 window centered on the key point. Each cell represents a pixel near the key point, the direction of the arrow represents the direction of the gradient in the pixel, and the length of the arrow represents the gradient. We reevaluate the performance by inverting the hexagram segments of the digital string, so that the current hexagram can choose the appropriate direction, so as to find a better solution in each iteration. The points in the saliency map have topological correspondence with the pixels in the input image. Different visual features (color, texture, direction, etc.) can be considered to have different contributions to saliency. Use visual features as vertical and horizontal vectors to create two-dimensional filter templates, also known as filters. For all benchmark problems, MFEA is much smaller than other algorithms in mean and variance, which shows that MFEA has strong advantages in solution accuracy and stability.


## 4.2. Analysis of Neural Evolution Algorithm with Different Multidimensional Factors

We use the single hidden layer neural network to fit the chaotic time series. Although the single hidden layer neural network has strong nonlinear fitting ability, it is easy to fall into the local optimum by using parameterλ. In general, the choice of this parameter depends on the experiments on salient object fields. The smaller λ is, the more parameter blocks may be considered as significant. Ashmolean_3 and Christchurch_5 in Oxford SK dataset are the query targets, and the hard allocation method (AKM + HA) is compared with the soft allocation method (AKM + SA), and the recall rate is obtained. The precision curve is shown in Figure 6.Figure 6
Accuracy curve.Because it is different from the exact semantic information expressed by ordinary natural scenery digital images, the characteristic of folk art digital images is that the semantic information it reflects and expresses is abstract. The template is convolved with the image. After convolution filtering, the image changes in the vertical and horizontal directions, and two hidden layer nodes are obtained. Different hidden layer nodes mean different task dimensions. The more hidden layer nodes, the higher the dimension of the task. Because of the dimension disaster, it becomes more difficult to optimize tasks in high-dimensional space, so we need to quickly transfer the information from low-dimensional networks to high-dimensional networks. In common search algorithms, the expression of the solution can take any form, generally without special treatment. But, in evolutionary computation, every solution of the original problem is regarded as a biological individual. The visual language model (VLM) is used to classify 10 target categories in CALTECH-256 image set in these visual dictionaries. The AP value of the target classification changes with the size of the visual dictionary as shown in Figure7.Figure 7
Effect of parameterk on the number of hash buckets and target classification accuracy.To begin, we choose three benchmark datasets from which to predict the mixed pure time series: Lorenz, Mackey glass, and sunspots. Computer simulations were used to create the Lorenz and Mackey glass datasets. The dataset is a real-world dataset that is relatively complex. The key points in the training image are detected using the dog operator, and the SIFT feature descriptor is obtained. The input folk art images are divided into regular image subregions using a simple spatial grid layout Image scale invariant attribute transformation descriptor based on the characteristics of folk art images. The feature clustering result obtained by combining feature points with pixels corresponds to a pixel region of the image in the clustering analysis of image features. The image extraction descriptor is realized by mapping clusters to pixel sets. For 10 different types of CALTECH-256 image sets, compare the time consumption of different dimensional multifactor neural evolutionary algorithms and AKM algorithms in constructing visual dictionaries. The experimental results are shown in Figure8.Figure 8
Comparison of time efficiency of different algorithms.Secondly, the fourth-order Runge-Kutta method is used to solve this differential equation, the integration time step is 0.01, the initial value of the equation is a random number between 0 and 5, and the first 30000 points are omitted to ensure the stability of the sequence. Assuming that the key points with similar spatial positions are similar, we can tie them together, calculate their quality, and regard them as a representative key point. Then, images with low entropy often contain a core object (i.e., significant areas are more obvious), while images with high entropy contain multiple objects under different textures (significant areas are not obvious or multiple areas attract attention). On the basis of each kind of mean value, the pixels close to each kind of mean value are classified into corresponding classes according to the principle of distance. All sample data are projected onto multiple models, respectively, and the probability of samples in corresponding categories is obtained. All sample data are projected on several models, respectively, and the probability of samples in the corresponding classes is obtained. The set of contemporary initial solutions and their transformed solutions is called candidate solution pool, which is labeled asHmap. Therefore, for a current state Sn, the candidate solution pool is the set of the current hexagram state and its state transformed by interleaving operator, inversion operator, and mutual operator.


## 5. Conclusions

A rich and colorful cultural tradition lies beneath an art form. This tradition, on the other hand, belongs to not only a bygone era but also to the present. It has an unnoticed effect on our daily lives and reflects the quality of our lives. We should pay close attention to the “meaning” of art graphics and study the differences in symbolic forms of Chinese traditional folk art graphics when applying them. The semantic gap between low-level image features and high-level meaning is, in general, one of the biggest problems in current intelligent image processing. It is one of the most important ideas for analyzing and extracting key semantic content from folk art images, combining semantic data with perceptual word packages, and solving the problem of semantic analysis and classification of folk art images. In evolutionary computation, the algorithm is aided by explicit or implicit migration. The composition and semantic expression of traditional folk art images using evolutionary computing technology are proposed, the image feature extraction and distance measurement methods are investigated, and the heterogeneous multifactor evolutionary algorithm and neural evolutionary algorithm are thoroughly examined. By analyzing the structural information of folk art images themselves, realizing the semantic classification of folk art images based on structure is also an important means to improve the semantic classification performance of folk art images. The proposed method weakens the problems of small sample and sample imbalance in traditional relevance feedback technology and improves the accuracy and efficiency of retrieval system.



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*Source: 8969212-2022-07-21.xml*