# Image Layout and Schema Analysis of Chinese Traditional Woodblock Prints Based on Texture and Color Texture Characteristics in the Environment of Few Samples

Authors: Xiaohong Yue
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Technology is the means by which all arts, including woodblock prints, are realized. The “kinship” with modern science and technology makes the development history of woodcut art that can also be understood as a technology history. The purpose of the texture expression produced in the creation of contemporary woodcut is to explore the rich texture expression forms made by contemporary representative painters using special material materials and tools in artistic creation, form a painting technique of personalized words, add new aesthetic meaning to art, and lay a foundation for the formation of unique style of Contemporary Art and the creation and development of woodcut texture. With the development of the times and the change of the public’s aesthetic taste, the traditional pattern of printmaking needs to be properly transformed if it is to adapt to the modern humanistic environment, which also involves the importance of screen layout and pattern analysis of Chinese traditional woodcut. Based on the analysis of texture and color texture features in a few-sample environment, this paper proposes an automatic classification method for vignetting texture pictures by extracting the corresponding vignetting coefficients, and through experiments to verify that the proposed SILCO has good generalization sex. In the algorithm designed in this paper, the experiment shows that the accuracy P has a 64.7% improvement effect, and the recallr has a 67.8% performance improvement. On the whole, the experimental data show that the comprehensive classification accuracy is more than 57.4%.


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## Body

## 1. Introduction

As a practical “tool” for information dissemination, ancient woodcut prints have gone through a process of social and cultural accumulation [1]. With the development of society and the needs of aesthetic culture, modern woodcut artists have enriched the representation of the patterns in woodcut prints and brought more space for exploration by integrating the different artistic characteristics of Chinese and Western prints in their creations [2]. In the development process of plastic arts, woodcut printmaking, as a unique art form, can effectively obtain a formal aesthetic effect through concrete and abstract expression by means of the changes of artistic language such as black, white, and gray [3]. In the context of a diversified cultural environment, people’s understanding of woodcut prints has gradually improved, and in the process of appreciating woodcut prints, it is different from the traditional visual mode, from the language art, visual art, and other woodcut prints, judging the actual value of woodcut prints from a comprehensive perspective [4]. From the perspective of word formation, “schema” includes “picture” and “form”, that is, the expression form of picture. Psychology expresses it as the existing frame structure in the human brain. In addition to representing psychologically known categories, the concept of schema is also used in artistic creation. Woodcut prints can be divided into ancient prints and modern prints in terms of development. Taking the Republic of China as the dividing line, the prints that copied Chinese paintings before the Republic of China were ancient prints. From Lu Xun’s advocacy of the “new woodcut movement” to modern times, it is the creation of prints [5]. In ancient copy prints, drawing, engraving, and printing were carried out by division of labor; modern prints are different, “it does not imitate, does not reproduce”, and the entire creative process is completed by the artist independently [6]. At the same time, major official exhibitions such as the National Art Exhibition and the National Printmaking Exhibition still pay attention to the type of printing and manual operation.Printmakers continue their creative practice along the path of ontology language in the field of creation, which makes the traditional plate making technology more and more exquisite [7]. Looking back on tradition has become a vein of current printmaking technology, as evidenced by the development of watermark woodcut and Mukou woodcut [8]. The practical exploration of woodcut texture is constantly innovating with the development of woodcut [9], as it is the most important form of expression in the contemporary art creation. With the continuous development of woodblock prints in China, painting materials, expression forms, and expression techniques are constantly developing and changing as a foreign art variety. The use of texture, a type of painting language, to emphasize the artist’s inner real feelings and the expression of subjective emotions is the key to contemporary art creation. The classification method [10, 11] cannot be directly applied in many fields due to the small sample size. In the case of sufficient data, actual projects face two additional challenges as a result of data updates. First, the data must be updated during the iteration process, and data collection, labeling, and sorting must be repeated numerous times. The small sample picture classification algorithm, in comparison to previous algorithms, has the advantage of obtaining the reasoning ability of “induction and summary” to deal with the use environment of small samples.This allows the model to respond flexibly to more variable usage environments, broadening the application scope of the deep learning model [12, 13]. The boundary conditions for using the model are expanded from the perspective of model users, the cost of building the model is reduced, and the effect of cost reduction and efficiency improvement is achieved to a certain extent. The goal of computer picture data analysis and image recognition [14, 15] is to detect and capture picture features, obtain picture quality data, and create a description of picture form and color in order to present the specific picture information. The essence of picture processing technology is the recombination of computer data coding. In order to achieve the goal of classification and detection, picture recognition relies on automatic selection of picture information features, comparison with other picture data, extracting different data, recombining, and classifying. The layout and schema analysis of traditional Chinese woodblock prints based on texture and color texture characteristics in a small-sample environment has not been addressed in the studies above. Therefore, this paper proposes the following innovations:①
The influence of vignetting model on texture feature index is analyzed. By extracting the corresponding vignetting coefficient, an automatic classification method of vignetting texture pictures is proposed, which reduces the influence of vignetting on the extracted feature index and improves the classification accuracy of vignetting texture pictures. The texture index is used to realize the quantitative representation of texture thickness, so as to realize the automatic selection of sample block size in drawing algorithm, and good practical results have been achieved.②
In order to enhance the practicability of few-shot detection and test the generalization performance of the algorithm proposed in this paper, several few-shot detection data sets were collected and sorted out, and experiments were conducted to verify that the SILCO proposed in this paper has good generalization.The introduction is the first chapter, and it explains the research background and significance of this paper, as well as the paper’s innovation. The second chapter summarizes relevant research findings from domestic and international literature and presents the paper’s research ideas. The method section is covered in the third chapter. This paper focuses on the picture layout and schema analysis of Chinese traditional woodblock prints in the context of a few samples, based on texture and color texture characteristics. The experiment and data processing section is covered in the fourth chapter. The data processing and accuracy are improved by the improved algorithm in this paper, which will greatly improve the reliability and rationality of the layout and schema analysis of traditional Chinese woodcuts. The fifth and final chapter is the epilogue. The proposed methods are summarized in this paper, and whether they are optimized in practice is examined.


## 2. Related Work

Qiu, Dai believe that with the continuous development of art commercialization, the creation method of woodblock is too single, and the innovation of woodblock graphic language is a major test faced by every woodblock creator. How to stand out in modern society and how to create novel and bright woodblock works is no longer the same, which is also the research and exploration direction of every woodblock creator [16]. Wang et al put forward that from the technical point of view, the general trend of current printmaking development can be summarized as: going to the intensive research of known technology, and going to the experiment of various media. The latter is the direct reflection of the technical trend of contemporary art. Contemporary art accepts various media and technologies with a “generalization” attitude, and embraces modern science and technology with great enthusiasm [17]. The research of Xu et al and others shows that the “texture” language, as an important form of expression, has also entered an era of free development and innovation, and the use of different tools, materials, and painting techniques has enriched a single form of artistic expression. Artists mix painting pigments with other non-painting tools and materials, such as iron wire, paper scraps, and foam, directly on the canvas to form a special picture effect [18]. Bu et al. and others believe that before classification and recognition in picture recognition, we should use the knowledge of digital image processing and computer graphics to preprocess the image, so as to carry out the following feature extraction and recognition. Image preprocessing includes binarization, image enhancement, image sharpening, and image segmentation [19]. Satt pointed out that the change of content and form and knife technique is the key to the transformation of woodcut prints. Creating content that is in line with the times, exploring new forms of expression, and incorporating one’s own feelings into the expression of knife technique may better reflect the aesthetic feeling of woodcut prints and express the creator’s ideas [20]. Sabzi et al.and others proposed that small sample categories should simulate the sample variance of similar categories when generating supplementary samples, and use this to build an objective function to optimize the entire model. The semantic consistency of samples with few-shot category samples adopts a recurrent generative network as its generative model [21]. Jing et al. proposed a single-stage method, which mainly includes SSD and Yolo methods, and their improved methods. If the area generated by the regressor is a network of multiple areas, the network generated by the regressor is classified into one area. Using multi-level features of SSD to predict can alleviate the multi-scale problem to some extent. SSD detects small objects on the large-scale feature map output by shallow network, but the semantic information of shallow network is less, which leads to the poor performance in small object detection [22]. Selim et al. believe that the research of texture feature extraction method is more and more in-depth, and its application is more and more extensive. On the one hand, researchers at home and abroad continue to conduct in-depth research on the existing texture feature extraction methods, and broaden their application in the field of practical engineering; on the other hand, researchers are committed to the fusion of existing texture feature extraction methods index for effective screening [23]. The research of Yi et al. and others found that “feelings are hidden in paintings, and feelings are related to paintings” is a classic overview of woodcut prints. In the process of China’s historical development, the thought of emphasizing agriculture and restraining commerce has affected the development of art to a certain extent. Some folk artists are not valued by the government, and their works are regarded as something to enjoy by some rich businessmen. Therefore, woodcut prints created in ancient times as book illustrations and New Year pictures are often not regarded as works of art [24]. Yamada et al. think that texture is one of the important features of a picture. Texture feature is a certain variation law of brightness or color between pixels in a picture visually, and it is a correlation of the overall variation of brightness and color in a local area of a picture [25]. Hu et al. and others proposed that schema is an important part of picture composition and a direct factor for the interpretation of visual art works. During the process of woodblock New Year pictures from its rise to the rise and fall of the times, its schemas have also changed from single to rich and diverse. From a broad perspective, it is mainly divided into five categories: exorcism and warding off evil spirits, praying for blessings and welcoming auspiciousness, opera legends, festive decorations, and living styles [26]. Garcia et al. and others believe that a series of related attributes such as reproducibility, plurality, indirectness, symbolism, communication, sociality, and publicity constitute the meta methodological system of reproducible art, including printmaking. Talking about printmaking from the perspective of technical attributes, this kind of research is based on the possibility of printmaking technology to artistic creation in the cognitive sense, and touches on the resource significance of “printing” technology in the process of achieving artists’ goals [27].On the basis of the above related research, the positive effect of the layout and schema analysis of traditional Chinese woodblock prints based on texture and color texture characteristics in a small sample environment is determined. A new algorithm for analyzing the layout and schema of traditional Chinese woodblock prints based on texture and color texture features is constructed, and a few-sample environment is used to conduct in-depth analysis and research on the layout and schema of woodblock prints, so as to use data more effectively. Explore the value hidden behind the feature data, and find out the true connotation of the layout and schema analysis of woodblock prints.


## 3. Methodology

### 3.1. A Review and Research on Related Theories

#### 3.1.1. The Basic Theory of Texture and Color Texture Characteristics

From the standpoint of the word “texture,” it refers to the high, low, staggered, rough, and smooth appearance of the tissue texture structure on the surface of an object, which creates a unique visual and tactile psychological sensation. Texture is a broad term that encompasses all visual phenomena on an object’s surface. Texture refers to a specific visual phenomenon formed by concave-convex changes on an object’s surface in a narrow sense. The texture characteristics mentioned here are mostly those found in nature. In woodblock painting, texture refers to the picture’s texture characteristics, such as the canvas’s texture structure, the juxtaposition, interleaving, and transformation of rich brush texture effects, and the visual feeling created by the stacking and multiple stacking of pigments themselves; the use of scraper technique, and the manifestation of the reference and utilization of various new materials.In the early days, Chinese woodcut prints mainly propagated religion by copying the Chinese paintings. In order to facilitate people’s understanding of the doctrine, the figuration of paintings became the demand of religious practical concepts. Only by faithfully “reproducing” religious content can we tell stories to the public, so as to facilitate the promotion of religion. The schema at this time carried the social norms of etiquette, and although the pictures presented were realistic, they had no personality. Color is an important visual feature of pictures. It is generally believed that the color and texture of pictures are unrelated to each other, and the color features and texture features are extracted and applied separately. In fact, the picture texture reflected by different color changes in the picture visually has obvious differences. After the color picture is converted into gray mode, there is a high similarity between the pictures with obvious differences, because according to the way of color conversion into gray, different colors with obvious differences may correspond to the same gray level.It is made up of points, lines, and surfaces that can be aggregated, lines that can be balanced or staggered, and surfaces that can be contrasted and tilted. It runs through the entire process of material cognition and has strong historical characteristics. Color is a source of light and space in materials. Color’s physical properties, combined with the illusion created by object interaction, have evolved into the most active language form in the use of materials. In people’s visual senses, the same material texture will elicit abstract psychological reactions and pleasures. People have different psychological reactions to different textures presented by different materials. Painting, engraving, and printing were divided into three categories in the early days of woodcut printmaking. Later, it was discovered that painting, engraving, and printing were all done by the same person. Printmakers could use their creative abilities to create prints to the fullest extent possible. Such prints are referred to as creative prints because they are reproductions of existing works. In fact, the development of the schematic language of woodcut prints took a long time. In the end, the reason is that the creation of woodcut prints involves a greater number of processes. Drawing, material selection, plate making, and printing are all part of the wood engraving creative process. Each step in the specific operation process must go through the re-creation process. Generally, woodcut will go through three stages: drawing, material selection, and plate making. Plate making is also the “re-creation” in the process of woodcut printmaking. The formation of the final graphic effect of woodcut prints lies in this link. In woodcut prints, the knife flavor and wood flavor of the woodcut print schema language are formed in this process.


#### 3.1.2. Classification of Paintings Based on Screen Layout and Schema

Because spectrum-based methods usually process the whole graph at the same time and are difficult to be parallel or extended to large graphs, space-based graph convolution networks have developed rapidly in recent years. Different from spectrum-based methods, space-based graph convolution network defines graph convolution as the process of gathering feature information from neighboring nodes. According to different methods of stacking convolutional layers, spatial-based graph convolutional neural networks can be further classified into two categories, recurrent-based graph convolutional neural networks and combinatorial-based graph convolutional neural networks. As shown in Figure1, it is a comparison between the recursive-based method and the combination-based method.Figure 1
Comparison of recursion-based methods and combinatorial-based methods.Obviously, in the process of gray conversion of pictures, due to the loss of color information, the difference of visual features between color pictures is reduced and the visual features of pictures are weakened in gray mode. Therefore, the color information and texture information of the picture should be fused to calculate the color-texture characteristics of the picture, so as to avoid the loss of color information caused by the gray scale conversion of the picture before texture extraction. Based on the above ideas, a color-texture feature extraction model for color pictures is proposed as shown in Figure2.Figure 2
Color texture feature extraction model.In this model, picture layering transforms the original picture into a group of pseudo gray pictures, and the extraction of texture features is transformed into the extraction of the texture of a group of pictures, which increases the time of texture feature extraction. Therefore, in practical application, the reasonable selection of pseudo gray image texture extraction method is very important to improve the efficiency of texture extraction. In this paper, IGabor transform is adopted to process the color and texture features of woodcut, in which the general expression of two-dimensional IGabor function is(1)guvx,y=k2σ2exp−k2x2+y22σ2.Using Gabor wavelet transform again, the process of extracting the texture features of the picture is as follows:(2)Fu,vx,y=f∗gu,vx,yMu,v=∫−∞+∞∫−∞+∞|Fu,vx,y|dxdyσu,v=∫−∞+∞∫−∞+∞|Fu,vx,y−Mu,v|2dxdy,where −g`+g is complex conjugate with each other. The texture features of the picture are composed of a group of Gabor filters with different directions and center frequencies, and the mean Mu,v and variance σu,v of energy distribution calculated according to the above process.


### 3.2. Detection and Analysis Based on Few Samples

Vignetting pictures can be considered as the result of the interaction between normal pictures and vignetting effects, and they can be extracted from vignetting pictures by certain methods. According to the vignetting model, the vignetting effectVx,y in the vignetting picture can be expressed as:(3)Vx,y=Ax,y×Gx,y×Tx,y.where Ax,y=1/1+r/f22 represents the off-axis illumination factor, r is the distance between pixel x,y and the center point of the picture, and f represents the effective focal length of the camera. The wavelet packet decomposition improves the picture information’s time-frequency resolution, makes full use of the texture picture’s detailed information, and can form an effective feature vector to more comprehensively and accurately describe the texture, resulting in a higher texture classification accuracy through the classifier. The wavelet packet function of function Wnxn=1,2 can be expressed as:(4)W2n=2∑k=02N−1hkWn2x−kW2n−1=2∑k=02N−1gkWn2x−kwhere W0x is the scaling function φx and W1x is the wavelet function ψx, the wavelet packet function composed of three parameters can be obtained as follows:(5)Wj,n,kx=2−j/2Wn2−jx−kwhere n∈N,j,k∈Z2.For each basis in the wavelet packet basis, it can be determined by the displacement of scale parameterj, translation parameter k, and vibration parameter n. At the same time, the wavelet packet is organized in a tree structure. The wavelet number structure of 2-layer wavelet packet decomposition is shown in Figure 3.Figure 3
Wavelet number of two-layer wavelet packet decomposition.In the process of testing, the SILCO algorithm does not need any labeling information of new categories. It can find out the common objects among several supporting pictures of the same category and label them with the detection box. On the basis of SSD, SILCO introduced spatial similarity module and feature re-weighting module. Since the image structure is not deformed by translation, the convolutional neural network can extract features by sliding the convolution kernel on the image. However, in the complex data scene environment, the typical graph structure does not have translation invariance, that is, the convolutional neural network cannot explore the graph structure inside or between the pictures, and the graph convolutional neural network was born in this background. The feature re-weighting value module weights the multiple values for different supporting pictures through the graph convolution network, so as to balance the influence of different supporting pictures as follows:(6)FRMS=σ(gcn(...gcnS))where σ is sigmoid layer. Gauss radial basis is introduced to compare with it.From this, we obtain the picture recognition results of surface defects based on color features in Table1 and the picture recognition results of surface defects based on the combination of color and texture features in Table 2.Table 1
Recognition results of surface defect pictures based on color features.
Kernel functionNumber of support vectorsAverage recognition rate (%)Polynomial5478.3Sigmoid10276.4Gauss radial basis7578.5Table 2
Recognition results of surface defect pictures based on the combination of color and texture features.
Kernel functionNumber of support vectorsAverage recognition rate (%)Polynomial4587.6Sigmoid8784.1Gauss radial basis7370.4It can be seen from the above table that the best way to make the texture feature recognition effect better is to use texture feature combined with color feature, so that the accuracy and calculation time are low in surface defect recognition.Calculate the Hadamard product ofFRMS′SSMqi;Si, connect the Hadamard product results of several supporting pictures in series, and sum and reduce the dimension. Its specific expression is as follows:(7)φqi,Si=RSCONCATj=1NSSMimjqi,Si⊗FRMSiThe aggregation module is used to aggregate and summarize the similar information of the query picture and each supporting picture. The aggregation method of element by element summation is adopted to summarize the similar areas of the query picture and multiple supporting pictures, so as to obtain the query picture features containing spatial attention information.


## 3.1. A Review and Research on Related Theories

### 3.1.1. The Basic Theory of Texture and Color Texture Characteristics

From the standpoint of the word “texture,” it refers to the high, low, staggered, rough, and smooth appearance of the tissue texture structure on the surface of an object, which creates a unique visual and tactile psychological sensation. Texture is a broad term that encompasses all visual phenomena on an object’s surface. Texture refers to a specific visual phenomenon formed by concave-convex changes on an object’s surface in a narrow sense. The texture characteristics mentioned here are mostly those found in nature. In woodblock painting, texture refers to the picture’s texture characteristics, such as the canvas’s texture structure, the juxtaposition, interleaving, and transformation of rich brush texture effects, and the visual feeling created by the stacking and multiple stacking of pigments themselves; the use of scraper technique, and the manifestation of the reference and utilization of various new materials.In the early days, Chinese woodcut prints mainly propagated religion by copying the Chinese paintings. In order to facilitate people’s understanding of the doctrine, the figuration of paintings became the demand of religious practical concepts. Only by faithfully “reproducing” religious content can we tell stories to the public, so as to facilitate the promotion of religion. The schema at this time carried the social norms of etiquette, and although the pictures presented were realistic, they had no personality. Color is an important visual feature of pictures. It is generally believed that the color and texture of pictures are unrelated to each other, and the color features and texture features are extracted and applied separately. In fact, the picture texture reflected by different color changes in the picture visually has obvious differences. After the color picture is converted into gray mode, there is a high similarity between the pictures with obvious differences, because according to the way of color conversion into gray, different colors with obvious differences may correspond to the same gray level.It is made up of points, lines, and surfaces that can be aggregated, lines that can be balanced or staggered, and surfaces that can be contrasted and tilted. It runs through the entire process of material cognition and has strong historical characteristics. Color is a source of light and space in materials. Color’s physical properties, combined with the illusion created by object interaction, have evolved into the most active language form in the use of materials. In people’s visual senses, the same material texture will elicit abstract psychological reactions and pleasures. People have different psychological reactions to different textures presented by different materials. Painting, engraving, and printing were divided into three categories in the early days of woodcut printmaking. Later, it was discovered that painting, engraving, and printing were all done by the same person. Printmakers could use their creative abilities to create prints to the fullest extent possible. Such prints are referred to as creative prints because they are reproductions of existing works. In fact, the development of the schematic language of woodcut prints took a long time. In the end, the reason is that the creation of woodcut prints involves a greater number of processes. Drawing, material selection, plate making, and printing are all part of the wood engraving creative process. Each step in the specific operation process must go through the re-creation process. Generally, woodcut will go through three stages: drawing, material selection, and plate making. Plate making is also the “re-creation” in the process of woodcut printmaking. The formation of the final graphic effect of woodcut prints lies in this link. In woodcut prints, the knife flavor and wood flavor of the woodcut print schema language are formed in this process.


### 3.1.2. Classification of Paintings Based on Screen Layout and Schema

Because spectrum-based methods usually process the whole graph at the same time and are difficult to be parallel or extended to large graphs, space-based graph convolution networks have developed rapidly in recent years. Different from spectrum-based methods, space-based graph convolution network defines graph convolution as the process of gathering feature information from neighboring nodes. According to different methods of stacking convolutional layers, spatial-based graph convolutional neural networks can be further classified into two categories, recurrent-based graph convolutional neural networks and combinatorial-based graph convolutional neural networks. As shown in Figure1, it is a comparison between the recursive-based method and the combination-based method.Figure 1
Comparison of recursion-based methods and combinatorial-based methods.Obviously, in the process of gray conversion of pictures, due to the loss of color information, the difference of visual features between color pictures is reduced and the visual features of pictures are weakened in gray mode. Therefore, the color information and texture information of the picture should be fused to calculate the color-texture characteristics of the picture, so as to avoid the loss of color information caused by the gray scale conversion of the picture before texture extraction. Based on the above ideas, a color-texture feature extraction model for color pictures is proposed as shown in Figure2.Figure 2
Color texture feature extraction model.In this model, picture layering transforms the original picture into a group of pseudo gray pictures, and the extraction of texture features is transformed into the extraction of the texture of a group of pictures, which increases the time of texture feature extraction. Therefore, in practical application, the reasonable selection of pseudo gray image texture extraction method is very important to improve the efficiency of texture extraction. In this paper, IGabor transform is adopted to process the color and texture features of woodcut, in which the general expression of two-dimensional IGabor function is(1)guvx,y=k2σ2exp−k2x2+y22σ2.Using Gabor wavelet transform again, the process of extracting the texture features of the picture is as follows:(2)Fu,vx,y=f∗gu,vx,yMu,v=∫−∞+∞∫−∞+∞|Fu,vx,y|dxdyσu,v=∫−∞+∞∫−∞+∞|Fu,vx,y−Mu,v|2dxdy,where −g`+g is complex conjugate with each other. The texture features of the picture are composed of a group of Gabor filters with different directions and center frequencies, and the mean Mu,v and variance σu,v of energy distribution calculated according to the above process.


## 3.1.1. The Basic Theory of Texture and Color Texture Characteristics

From the standpoint of the word “texture,” it refers to the high, low, staggered, rough, and smooth appearance of the tissue texture structure on the surface of an object, which creates a unique visual and tactile psychological sensation. Texture is a broad term that encompasses all visual phenomena on an object’s surface. Texture refers to a specific visual phenomenon formed by concave-convex changes on an object’s surface in a narrow sense. The texture characteristics mentioned here are mostly those found in nature. In woodblock painting, texture refers to the picture’s texture characteristics, such as the canvas’s texture structure, the juxtaposition, interleaving, and transformation of rich brush texture effects, and the visual feeling created by the stacking and multiple stacking of pigments themselves; the use of scraper technique, and the manifestation of the reference and utilization of various new materials.In the early days, Chinese woodcut prints mainly propagated religion by copying the Chinese paintings. In order to facilitate people’s understanding of the doctrine, the figuration of paintings became the demand of religious practical concepts. Only by faithfully “reproducing” religious content can we tell stories to the public, so as to facilitate the promotion of religion. The schema at this time carried the social norms of etiquette, and although the pictures presented were realistic, they had no personality. Color is an important visual feature of pictures. It is generally believed that the color and texture of pictures are unrelated to each other, and the color features and texture features are extracted and applied separately. In fact, the picture texture reflected by different color changes in the picture visually has obvious differences. After the color picture is converted into gray mode, there is a high similarity between the pictures with obvious differences, because according to the way of color conversion into gray, different colors with obvious differences may correspond to the same gray level.It is made up of points, lines, and surfaces that can be aggregated, lines that can be balanced or staggered, and surfaces that can be contrasted and tilted. It runs through the entire process of material cognition and has strong historical characteristics. Color is a source of light and space in materials. Color’s physical properties, combined with the illusion created by object interaction, have evolved into the most active language form in the use of materials. In people’s visual senses, the same material texture will elicit abstract psychological reactions and pleasures. People have different psychological reactions to different textures presented by different materials. Painting, engraving, and printing were divided into three categories in the early days of woodcut printmaking. Later, it was discovered that painting, engraving, and printing were all done by the same person. Printmakers could use their creative abilities to create prints to the fullest extent possible. Such prints are referred to as creative prints because they are reproductions of existing works. In fact, the development of the schematic language of woodcut prints took a long time. In the end, the reason is that the creation of woodcut prints involves a greater number of processes. Drawing, material selection, plate making, and printing are all part of the wood engraving creative process. Each step in the specific operation process must go through the re-creation process. Generally, woodcut will go through three stages: drawing, material selection, and plate making. Plate making is also the “re-creation” in the process of woodcut printmaking. The formation of the final graphic effect of woodcut prints lies in this link. In woodcut prints, the knife flavor and wood flavor of the woodcut print schema language are formed in this process.


## 3.1.2. Classification of Paintings Based on Screen Layout and Schema

Because spectrum-based methods usually process the whole graph at the same time and are difficult to be parallel or extended to large graphs, space-based graph convolution networks have developed rapidly in recent years. Different from spectrum-based methods, space-based graph convolution network defines graph convolution as the process of gathering feature information from neighboring nodes. According to different methods of stacking convolutional layers, spatial-based graph convolutional neural networks can be further classified into two categories, recurrent-based graph convolutional neural networks and combinatorial-based graph convolutional neural networks. As shown in Figure1, it is a comparison between the recursive-based method and the combination-based method.Figure 1
Comparison of recursion-based methods and combinatorial-based methods.Obviously, in the process of gray conversion of pictures, due to the loss of color information, the difference of visual features between color pictures is reduced and the visual features of pictures are weakened in gray mode. Therefore, the color information and texture information of the picture should be fused to calculate the color-texture characteristics of the picture, so as to avoid the loss of color information caused by the gray scale conversion of the picture before texture extraction. Based on the above ideas, a color-texture feature extraction model for color pictures is proposed as shown in Figure2.Figure 2
Color texture feature extraction model.In this model, picture layering transforms the original picture into a group of pseudo gray pictures, and the extraction of texture features is transformed into the extraction of the texture of a group of pictures, which increases the time of texture feature extraction. Therefore, in practical application, the reasonable selection of pseudo gray image texture extraction method is very important to improve the efficiency of texture extraction. In this paper, IGabor transform is adopted to process the color and texture features of woodcut, in which the general expression of two-dimensional IGabor function is(1)guvx,y=k2σ2exp−k2x2+y22σ2.Using Gabor wavelet transform again, the process of extracting the texture features of the picture is as follows:(2)Fu,vx,y=f∗gu,vx,yMu,v=∫−∞+∞∫−∞+∞|Fu,vx,y|dxdyσu,v=∫−∞+∞∫−∞+∞|Fu,vx,y−Mu,v|2dxdy,where −g`+g is complex conjugate with each other. The texture features of the picture are composed of a group of Gabor filters with different directions and center frequencies, and the mean Mu,v and variance σu,v of energy distribution calculated according to the above process.


## 3.2. Detection and Analysis Based on Few Samples

Vignetting pictures can be considered as the result of the interaction between normal pictures and vignetting effects, and they can be extracted from vignetting pictures by certain methods. According to the vignetting model, the vignetting effectVx,y in the vignetting picture can be expressed as:(3)Vx,y=Ax,y×Gx,y×Tx,y.where Ax,y=1/1+r/f22 represents the off-axis illumination factor, r is the distance between pixel x,y and the center point of the picture, and f represents the effective focal length of the camera. The wavelet packet decomposition improves the picture information’s time-frequency resolution, makes full use of the texture picture’s detailed information, and can form an effective feature vector to more comprehensively and accurately describe the texture, resulting in a higher texture classification accuracy through the classifier. The wavelet packet function of function Wnxn=1,2 can be expressed as:(4)W2n=2∑k=02N−1hkWn2x−kW2n−1=2∑k=02N−1gkWn2x−kwhere W0x is the scaling function φx and W1x is the wavelet function ψx, the wavelet packet function composed of three parameters can be obtained as follows:(5)Wj,n,kx=2−j/2Wn2−jx−kwhere n∈N,j,k∈Z2.For each basis in the wavelet packet basis, it can be determined by the displacement of scale parameterj, translation parameter k, and vibration parameter n. At the same time, the wavelet packet is organized in a tree structure. The wavelet number structure of 2-layer wavelet packet decomposition is shown in Figure 3.Figure 3
Wavelet number of two-layer wavelet packet decomposition.In the process of testing, the SILCO algorithm does not need any labeling information of new categories. It can find out the common objects among several supporting pictures of the same category and label them with the detection box. On the basis of SSD, SILCO introduced spatial similarity module and feature re-weighting module. Since the image structure is not deformed by translation, the convolutional neural network can extract features by sliding the convolution kernel on the image. However, in the complex data scene environment, the typical graph structure does not have translation invariance, that is, the convolutional neural network cannot explore the graph structure inside or between the pictures, and the graph convolutional neural network was born in this background. The feature re-weighting value module weights the multiple values for different supporting pictures through the graph convolution network, so as to balance the influence of different supporting pictures as follows:(6)FRMS=σ(gcn(...gcnS))where σ is sigmoid layer. Gauss radial basis is introduced to compare with it.From this, we obtain the picture recognition results of surface defects based on color features in Table1 and the picture recognition results of surface defects based on the combination of color and texture features in Table 2.Table 1
Recognition results of surface defect pictures based on color features.
Kernel functionNumber of support vectorsAverage recognition rate (%)Polynomial5478.3Sigmoid10276.4Gauss radial basis7578.5Table 2
Recognition results of surface defect pictures based on the combination of color and texture features.
Kernel functionNumber of support vectorsAverage recognition rate (%)Polynomial4587.6Sigmoid8784.1Gauss radial basis7370.4It can be seen from the above table that the best way to make the texture feature recognition effect better is to use texture feature combined with color feature, so that the accuracy and calculation time are low in surface defect recognition.Calculate the Hadamard product ofFRMS′SSMqi;Si, connect the Hadamard product results of several supporting pictures in series, and sum and reduce the dimension. Its specific expression is as follows:(7)φqi,Si=RSCONCATj=1NSSMimjqi,Si⊗FRMSiThe aggregation module is used to aggregate and summarize the similar information of the query picture and each supporting picture. The aggregation method of element by element summation is adopted to summarize the similar areas of the query picture and multiple supporting pictures, so as to obtain the query picture features containing spatial attention information.


## 4. Result Analysis and Discussion

The results were measured by MAP. MAP is an indicator that can reflect the global performance. There are two basic indicators in the field of classification and recognition: recall rate and accuracy rate. However, these two basic indicators have the limitation of single point value. The calculation methods of precision and recall are shown in Table3.Table 3
Calculation of precision and recall.
CorrelationAccuracyRetrievedAP = A/(A + B)Not retrievedCRecallR = A/(A + C)Accuracy and recall affect each other. Usually, by setting different thresholds MAP1 = 0.52 and MAP2 = 0.64, the accuracy and recall are counted and the P-R data diagram is obtained, as shown in Figure4.Figure 4
Recall analysis.In the P-R diagram, it is easy to see that the performance of the upper system is better than that of the lower system, although the performance curves of the two systems overlap and cross. Figure5Assuming the ideal situation, the area between the curve and the coordinate axis should be 1. And in the above data graph, the two thresholds under the recall rate have good performance, which also shows that the algorithm designed in this paper has a good effect and is universal. In the accuracy rate, it can be found that there are large fluctuations in the 2–3 interval, but the subsequent improvement is very rapid. This is due to the difference in the value of the threshold, which leads to the extremely increase of the interference item in a certain period of time. Due to the lag of the screening system, the accumulated interference will appear more intensively. However, due to the superiority of the algorithm, the interference item will be eliminated soon, so there will be regular fluctuation in the subsequent pictures. Therefore, the accuracy rate P has a 64.7% improvement effect, and the recall rate R also has a 67.8% performance improvement.Figure 5
Accuracy analysis.Based on the above analysis of recall rate and accuracy, the next step will be to study the feasibility, the efficiency of screen layout, and schema extraction, and the correct rate of color and texture classification. Figure6shows the experimental analysis on sample sets C1 and C2.Figure 6
Feasibility analysis.The overall feasibility is maintained at more than 30% in the above figures, which ensures the basic requirements of the experiment and demonstrates that the method proposed in this paper can be carried out under general conditions and reasonable conclusions can be drawn. The accuracy and efficiency of extracting different feature points in woodblock Figure7prints can also provide convenience for time and actual operation, and the extraction efficiency of picture layout and schema is a deepening index, which can reflect the working efficiency of the entire algorithm. Color and texture classification reflects the overall understanding Figure 8of the painting, which will influence the quality of the work in practice. As a result, the classification accuracy is subject to algorithm optimization, and there is enough experimental data to obtain a comprehensive classification. The accuracy rate is higher than 57.4%.Figure 7
Analysis of screen layout and schema extraction efficiency.Figure 8
The correct rate of color texture classification.


## 5. Conclusions

The ability to classify color texture features is greater than that of gray texture features. Although picture layering increases the texture feature extraction time, the color texture feature extraction time of the entire picture does not increase significantly because the number of filters is reduced by the IGabor transform. During the creation of a woodcut print, the artist imbues the image with all of his spiritual hopes, causing the originally solemn image to elicit a different kind of emotion in viewers and the vitality of art to merge and release. In terms of improving learning motivation, triggering time investment, reducing cognitive load, and improving sublimation effect, screen layout has both positive and complex effects. The tabbed layout method, which presents knowledge in blocks based on an internal logic, is more conducive to motivating learners, reducing cognitive load, and improving learning effect than the waterfall layout method, which presents knowledge as a whole. The formal beauty of woodcut works is enhanced by the aesthetic feeling of texture presented by the surface structure of the work. The organic combination of stroke expression language and foundation in the picture creates an overall artistic effect. Furthermore, the experimental results for the algorithm proposed in this paper show that the accuracy rate P improves by 64.7 percent, while the recall rateR improves by 67.8 percent. Overall, experimental data exist, and it can be concluded that the comprehensive classification accuracy rate is greater than 57.4%.



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*Source: 8008796-2022-07-11.xml*