# Study on the Innovative Development of Digital Media Art in the Context of Artificial Intelligence

Authors: Chaomiao Chen
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, so that art under digital media has the characteristics of intelligence, networking, and content diversification, so that it can be better applied in modern digital media communication and let visual art reach new development heights in the context of digital media. The current rapid development of artificial intelligence technology is being gradually applied to all fields of society. From the current point of view, China’s digital media art will inevitably remain in the early development stage for a long time to face many difficulties and problems. In the context of the era of artificial intelligence, there will also be a large number of art and design talents for continuous research and exploration, and the emergence of these talents will certainly prompt the further development and enhancement of digital media art and artificial intelligence technology. In this process, it is especially worth noting that China’s excellent cultural connotations should not be abandoned. Committed to digging more beneficial digital media art elements from traditional culture to make digital media art always develop in a more meaningful way. This paper takes the artificial intelligence era as the background, discusses the core of the development of the mutual integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, as well as the innovative development direction and future trends, proposes a digital media art design algorithm based on the convolutional neural network, and finally proves the effectiveness of the method in the relevant data set.


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## Body

## 1. Introduction

With the increasing development of current science and technology and social economy, the art of combining digital and multimedia has gradually developed and matured, while gradually replacing traditional art methods and becoming a mainstream art. At present, it is necessary to accelerate the pace of development, reform, and innovation for the concept of art, and to fully display the characteristics of the times of the art of combining digital and multimedia. First, the need for innovation in the art of combining digital and multimedia concept, the art of combining digital and multimedia in the actual development process is still not optimistic, for example, the process of product design is seriously lacking artistry, often just applying the template to carry out design work, which is conducive to people to change the traditional cognition of the art of combining digital and multimedia [1–3]. The art of combining digital and multimedia is a comprehensive discipline, which involves many aspects of content and knowledge; however, the art of combining digital and multimedia has not yet achieved interdisciplinary communication, but is relatively rigid in the level of artistic expression; for example, shooting a certain short film not only need to have a camera but also need to create, tuning, rendering, color mixing, etc., which involves relatively cumbersome content, so, at present, the art of combining digital and multimedia development among the change of mindset is particularly urgent. The innovative development of digital media art in the context of artificial intelligence is shown in Figure 1.Figure 1
Schematic diagram of the innovative development of digital media art in the context of artificial intelligence.To achieve further innovation and reform of the concept of the art of combining digital and multimedia, the original traditional artistic thinking and design methods should be abandoned, and the art of combining digital and multimedia with cultural connotations and contemporary values should be created [4–6]. However, thought itself has a significant dynamic character, but is also particularly susceptible to the internal and external environment, at the same time, human epiphany, association, and intuition usually have a certain influence on the thought, in order to completely out of the traditional art thought on the human fetters, need to combine with the development of the times, to cognitive and understand the new works, feel the vitality of the times, should also experience life, should only in this way can we achieve change and innovation in the art of combining digital and multimedia. In the present, artistic expression includes many types, for example, transplantation, analogy, and imagination. If traditional ideas are used in the current process of the art of combining digital and multimedia development, it is difficult to effectively meet people’s pursuit of art and requires a combination of basic artistic expression methods to promote the enrichment of the art of combining digital and multimedia connotations. First, imagination is not unrealistic but should be based on the premise of rich knowledge through the brain to effectively create and process the process. For example, if a movie is imaginative and out of touch with reality, it is unlikely to have such a great impact [7]. Although the content described in the movie is far from reality, it is still created with reality as the basis. Secondly, an analogy is mainly from the nature between two similar things, inferring things in other aspects of different points, and the same point, decision-making, by using the analogy, can often get good results. Finally, transplantation is an excellent creative technique that usually facilitates the innovation of ideas. Other techniques that are similar to transplantation include transformation, which is literally the conversion of things to each other, including conceptual and role shifts. The main content is that life is art. Nowadays, people’s material living standard has improved significantly, and the understanding of aesthetics has also undergone a great change, so art is not only exclusive to the privileged class, but everyone can create art and appreciate art, which is the test of life. Third, the expression of the art of combining digital and multimedia concept innovation is first of all the content. According to the traditional artworks for comparison and analysis, we can fully understand that the content of early artworks is relatively single. With the increasing development of the times and economy, and the mutual integration of Eastern and Western cultures, people can see more content with unique artistic characteristics. The second is the method of creation. The rapid development of science and technology, as well as the increasing maturity of computer technology, has enabled the creation of digital media with more diverse characteristics. For example, Arlen creation software is a typical representative. Creators can practice their artistic ideas through Arlen creative software. In addition, there are still a large number of the art of combining digital and multimediatizes experimenting with other methods of art creation [8].The world has entered the era of digital development, and the fusion of artificial intelligence technology and the art of combining digital and multimedia is rapidly reconstructing and overturning people’s perceptions of the art and design field, and new means of design and artistic expression have been created. According to the “2017 Emerging Technology Maturity Curve Report” released by a U.S. consulting firm, artificial intelligence technology had a share of over 50% of the 33 new technologies at that time [9–12]. In these subsequent years, AI technology has achieved a comprehensive and widespread application. Nowadays, AI technology is compatible with intelligent systems such as environment perception, memory storage, thinking and reasoning, and learning ability possessed by human intelligence. And in the field of design, AI-aided design has made certain achievements, such as the artificial intelligence product Laban launched by Alibaba in the Double Eleven, which designed 4 × 108 advertising banners by itself, witnessing the powerful power of art and technology collision. However, in the process of artificial intelligence replacing the basic design of artwork, people also perceive the lack of judgment and understanding of aesthetics by intelligent machines, and the beauty of art cannot be realized by computer technology alone. At this point, art designers became “trainers,” they need to cultivate the computer’s sense of beauty so that the computer can design and learn to acquire a kind of human-based aesthetics and wisdom, so as to form their own thinking and insights on design aesthetics, and then feedback to the works involved. The optimists believe that the future of the art of combining digital and multimedia has unlimited possibilities, while the pessimists believe that it will be the end of the road for art designers. Along with the accelerated pace of global digital development, Chinese digital media design majors should take the initiative to accept new knowledge and technology in education, objectively and rationally view the opportunities and challenges brought by artificial intelligence and its high technology, and always uphold the principles of integrating tradition and modernity, approaching ideals and reality, giving equal importance to practice and theory, and carrying out reform and survival, so as to build a digital media design teaching system that meets the modern context. This paper proposes a system based on the convolutional neural network. The main contributions of this paper are (1) A convolutional neural network-based art design algorithm for combining digital and multimedia is proposed, the framework structure of the neural network model is analyzed, and the process of how to use the model to extract artistic styles and fuse them with ordinary images is discussed. (2) Then, based on the theory, according to the actual characteristics of the art combining digital and multimedia, the experimental analysis is performed to find the processing content images with suitable convolution layers, as well as finding the best overlay combination for digital media art feature extraction and proposing visualization criteria for evaluating image quality. (3) Finally, the feasibility of the theory is verified by adjusting the scale coefficients of content images and style images to obtain images that meet the expected goals, and a new style extraction method for digital and multimedia combined art is proposed.


## 2. Related Work

### 2.1. Digital Media Art

The art of combining digital and multimedia refers to the synthesis of digital image processing technology, information and communication technology, art design, and other disciplines. Unlike traditional art, the art of combining digital and multimedia is widely used and developed in other fields through media communication with its own unique characteristics of integration, virtualization, and intersectionality [12–14]. The art of combining digital and multimedia has diversified forms of expression, and it changes people’s aesthetic pursuit of art with its unique creation concept, influencing the direction of art modernity development. As socialism with Chinese characteristics enters a new era, the living standards of the people and the aesthetic ability of the public are increasingly rising, the art of combining digital and multimedia not only brings people a better quality visual experience but also gives them stronger sensory stimulation. In artistic expression and communication, the art of combining digital and multimedia can vividly and effectively release and convey information and is gradually becoming a new carrier for the development of modern artistic expression. The benign development of the art of combining digital and multimedia can play an excellent role in promoting the development of human society. The main characteristics of the art of combining digital and multimedia along with the development of social life, people have put forward higher requirements for the accuracy, interactivity, and efficiency of information dissemination, and the application of the art of combining digital and multimedia in art has brought about great changes in people’s artistic feelings. The art of combining digital and multimedia is an art that combines participation, interactivity, multimedia use, high-tech enrichment, and new forms of expression, and is a reorganization of the real world and virtual images. Its emergence and development has provided a broader stage of expression for traditional art content. Grassroots participation in art is closely related to people’s lives, and people can enjoy their favorite artworks at any time, publish their own comments on artworks through the Internet, and even publish their own artworks anonymously. 3D and 4D technologies that were only bred in the digital media era, have become popular, extending people’ ability to appreciate art, innovating forms of art appreciation, and getting closer to art. The interactive nature of two-way interactive is the art of combining digital and multimedia.The interactive nature of two-way interactive is the art of combining digital and multimedia provides a new creative experience for art creators. Before the digital media came out, the dissemination of art was a whole process of creation, release, and transmission by artists and traditional media, with the audience only playing the role of receiver and appreciator, in which the artist could not get feedback from the audience. The emergence of digital media has brought about a new change in the process of art communication [15–17]. The whole process has changed from one-way to two-way interaction, and the artist can also know the feelings of the audience at any time, and the audience can also participate in the creation of the process with two-way interaction. Multimedia use of our country’s traditional artworks are created through different tools and different materials, for now, China’s art of combining digital and multimedia products are mainly conducted through computers and new technologies. In the process of painting oil paintings, the creators first use computer software, which will help with later editing and modifications, and the computer can record the entire creative process of the creator. In addition to these, the art of combining digital and multimedia also includes e-books and e-maps. People use e-books for reading and can make comments and changes at any time, bringing convenience to reading. People can enter the places they want to go on the electronic map, and it will remind people of the relevant traffic precautions. One of the most important features of high-tech enrichment in the field of art is exaggeration, and the art of combining digital and multimedia is no exception, and it brings this feature to the highest point. We can easily find that in the art of combining digital and multimedia, every piece of art and every character is exaggerated, with exaggerated expressions and shapes, and even exaggerated language, all of which are designed to give people the best artistic experience. For example, new technologies in the art of combining digital and multimedia are used in paintings to make them more vivid and rich; VR games are games with high technology in the art of combining digital and multimedia, and the game screen becomes three-dimensional from two-dimensional, so that people can participate in the game more realistically. Compared with other types of artistic expression, the art of combining digital and multimedia has its own unique innovative performance, which is summarized in the following points: innovation of creative concepts China’s economy in the new era is still in a relatively rapid development period, and the strong economic level provides convenient conditions for the innovative development of the art of combining digital and multimedia. The economic foundation determines the superstructure, with the economic foundation as a pavement, and then through new technology and means, the art of combining digital and multimedia can have adequate development concepts and new ideas. Under the new creation concept, the traditional means and the current means will be very different, the creation method is more and more rich and diverse, the creation content is newer, the art of combining digital and multimedia also pays more attention to the development of personality, and the needs of different audience groups are satisfied. The difficulty of creation lies in new ideas, once any creation lacks new ideas, it is difficult to get people’s love, and the art of combining digital and multimedia is no exception [17–19]. With the emergence of the art of combining digital and multimedia, some old artists do not have a deep enough understanding of this new form of creation and cannot break through themselves in time with the development of the times. Some people still use the old methods in their creation and simply think about the development of traditional art according to their past thinking. In their eyes, the modern art of combining digital and multimedia has no way to reflect the real value of their works. This makes the works they create lack their own individuality and are as same. Besides these people, there are also some artists who are not too familiar with the functions of digital media, leading to the lack of innovation in their works and the phenomenon of similar works.


### 2.2. Artificial Intelligence Technology

The integration of artificial intelligence technology and the art of combining digital and multimedia is based on human understanding of themselves and their experience of emotions [19–21]. If a person has no understanding of the formation of aesthetic thinking and consciousness in a different cultural context, it is simply impossible to solve various design problems concerning the way humans live and work if they simply rely on the computational methods of intelligent machines. Today’s iterative developments in science and technology continue to enrich the way people live and produce and raise the demand for aesthetics. The reason for developing artificial intelligence technology is to better serve people themselves, while the integration of art and design is precisely from the emotional needs of people, where art is more prominent in the expression of the subjective concept of people. The future of artificial intelligence is to think about the needs of people and the ability to change different environmental needs. From the current application of artificial intelligence scenarios, artificial intelligence in solving certain boundary problems, people are bound to lose to the machine; however, the need to make decisions with the help of emotional judgment, the logical reasoning made based on artificial intelligence big data also exists uncertainty. At present, artificial intelligence can only provide high technology, while the formation of excellent artworks must require new technical means to achieve. People have emphasized that art is the expression of human subjective concepts, just like saying that if it is similar, it is vulgar, and if it is not, it is not. Art does not have boundaries; therefore, artificial intelligence can never replace a human to complete subjective concepts. The key to the integration of artificial intelligence technology and the art of combining digital and multimedia is to obtain new methods of art created with the help of artificial intelligence technology, so as to better enrich the means of the art design, so the core of the integration of the two depends on people’s values and cognitive ability of artificial intelligence [22].In summary, we know the concept of artificial intelligence, the way of integration of artificial intelligence and the art of combining digital and multimedia and the core significance. In the context of artificial intelligence, intelligent machines can drive the continuous development and progress of society by virtue of their powerful computing power, and the next stage of deeper integration and innovation of artificial intelligence and the art of combining digital and multimedia will definitely present the following development characteristics. The art of combining digital and multimedia has characteristics such as openness, integration, and interactivity, and there are also artistic time-sensitive features that change with time or forms, and it is based on these special features that make the current art of combining digital and multimedia more and more diversified. In recent years, the rapid development of computer and network technology in China has reached the needs of art creation making the organic integration of art and artificial intelligence technology. Only by scientifically and rationally utilizing this fusion can the art of combining digital and multimedia give audiences a more perfect visual experience and strengthen their perceptions and impressions of the works of art creation. With various high-tech as the creative support of the art of combining digital and multimedia, it will let art and technology burst into more artistic miracles. Actively expanding the diversity of digital media, the characteristics of the art of combining digital and multimedia are more diverse, yet in fact, the development of the art of combining digital and multimedia and artificial intelligence technology shows an obvious sense of weakness. In this period of sustainable development of the art of combining digital and multimedia and simultaneous technological progress, Chinese art of combining digital and multimedia will present more new types of art in a richer form of expression, prompting digital media to achieve diverse development, bringing audiences a completely different consumer experience and a new experience and artistic perceptiveness in visual perception [23–25]. From this point of view, the mutual integration of the art of combining digital and multimedia and artificial intelligence technology will certainly make the development space of art and technology more extensive. From the analysis of the development history of the art of combining digital and multimedia in the past, the integration of the art of combining digital and multimedia and artificial intelligence technology in China can only be better presented in the form of technology and art through digital media by continuously strengthening its own traditional cultural heritage and modern civilization. Most people one-sidedly believe that digital technology and computer technology is the catalyst for the development of the art of combining digital and multimedia and technology, however, in fact from another perspective, it is based on the background of the artificial intelligence era that society has stepped into a new period of development of art and technology. From the current point of view, Chinese art of combining digital and multimedia is bound to remain in the early stages of development for a long time and to face many difficulties and problems. In the context of the artificial intelligence era, there will also be a large number of art and design talents emerging for continuous research and exploration, and the emergence of these talents will certainly lead to further development and enhancement of the art of combining digital and multimedia and artificial intelligence technology. In this process, it is especially worth noting that the excellent Chinese cultural connotations should not be abandoned. Commitment to digging out more beneficial art of combining digital and multimedia elements from traditional culture is the only way to make the art of combining digital and multimedia always develop in a more meaningful way.


## 2.1. Digital Media Art

The art of combining digital and multimedia refers to the synthesis of digital image processing technology, information and communication technology, art design, and other disciplines. Unlike traditional art, the art of combining digital and multimedia is widely used and developed in other fields through media communication with its own unique characteristics of integration, virtualization, and intersectionality [12–14]. The art of combining digital and multimedia has diversified forms of expression, and it changes people’s aesthetic pursuit of art with its unique creation concept, influencing the direction of art modernity development. As socialism with Chinese characteristics enters a new era, the living standards of the people and the aesthetic ability of the public are increasingly rising, the art of combining digital and multimedia not only brings people a better quality visual experience but also gives them stronger sensory stimulation. In artistic expression and communication, the art of combining digital and multimedia can vividly and effectively release and convey information and is gradually becoming a new carrier for the development of modern artistic expression. The benign development of the art of combining digital and multimedia can play an excellent role in promoting the development of human society. The main characteristics of the art of combining digital and multimedia along with the development of social life, people have put forward higher requirements for the accuracy, interactivity, and efficiency of information dissemination, and the application of the art of combining digital and multimedia in art has brought about great changes in people’s artistic feelings. The art of combining digital and multimedia is an art that combines participation, interactivity, multimedia use, high-tech enrichment, and new forms of expression, and is a reorganization of the real world and virtual images. Its emergence and development has provided a broader stage of expression for traditional art content. Grassroots participation in art is closely related to people’s lives, and people can enjoy their favorite artworks at any time, publish their own comments on artworks through the Internet, and even publish their own artworks anonymously. 3D and 4D technologies that were only bred in the digital media era, have become popular, extending people’ ability to appreciate art, innovating forms of art appreciation, and getting closer to art. The interactive nature of two-way interactive is the art of combining digital and multimedia.The interactive nature of two-way interactive is the art of combining digital and multimedia provides a new creative experience for art creators. Before the digital media came out, the dissemination of art was a whole process of creation, release, and transmission by artists and traditional media, with the audience only playing the role of receiver and appreciator, in which the artist could not get feedback from the audience. The emergence of digital media has brought about a new change in the process of art communication [15–17]. The whole process has changed from one-way to two-way interaction, and the artist can also know the feelings of the audience at any time, and the audience can also participate in the creation of the process with two-way interaction. Multimedia use of our country’s traditional artworks are created through different tools and different materials, for now, China’s art of combining digital and multimedia products are mainly conducted through computers and new technologies. In the process of painting oil paintings, the creators first use computer software, which will help with later editing and modifications, and the computer can record the entire creative process of the creator. In addition to these, the art of combining digital and multimedia also includes e-books and e-maps. People use e-books for reading and can make comments and changes at any time, bringing convenience to reading. People can enter the places they want to go on the electronic map, and it will remind people of the relevant traffic precautions. One of the most important features of high-tech enrichment in the field of art is exaggeration, and the art of combining digital and multimedia is no exception, and it brings this feature to the highest point. We can easily find that in the art of combining digital and multimedia, every piece of art and every character is exaggerated, with exaggerated expressions and shapes, and even exaggerated language, all of which are designed to give people the best artistic experience. For example, new technologies in the art of combining digital and multimedia are used in paintings to make them more vivid and rich; VR games are games with high technology in the art of combining digital and multimedia, and the game screen becomes three-dimensional from two-dimensional, so that people can participate in the game more realistically. Compared with other types of artistic expression, the art of combining digital and multimedia has its own unique innovative performance, which is summarized in the following points: innovation of creative concepts China’s economy in the new era is still in a relatively rapid development period, and the strong economic level provides convenient conditions for the innovative development of the art of combining digital and multimedia. The economic foundation determines the superstructure, with the economic foundation as a pavement, and then through new technology and means, the art of combining digital and multimedia can have adequate development concepts and new ideas. Under the new creation concept, the traditional means and the current means will be very different, the creation method is more and more rich and diverse, the creation content is newer, the art of combining digital and multimedia also pays more attention to the development of personality, and the needs of different audience groups are satisfied. The difficulty of creation lies in new ideas, once any creation lacks new ideas, it is difficult to get people’s love, and the art of combining digital and multimedia is no exception [17–19]. With the emergence of the art of combining digital and multimedia, some old artists do not have a deep enough understanding of this new form of creation and cannot break through themselves in time with the development of the times. Some people still use the old methods in their creation and simply think about the development of traditional art according to their past thinking. In their eyes, the modern art of combining digital and multimedia has no way to reflect the real value of their works. This makes the works they create lack their own individuality and are as same. Besides these people, there are also some artists who are not too familiar with the functions of digital media, leading to the lack of innovation in their works and the phenomenon of similar works.


## 2.2. Artificial Intelligence Technology

The integration of artificial intelligence technology and the art of combining digital and multimedia is based on human understanding of themselves and their experience of emotions [19–21]. If a person has no understanding of the formation of aesthetic thinking and consciousness in a different cultural context, it is simply impossible to solve various design problems concerning the way humans live and work if they simply rely on the computational methods of intelligent machines. Today’s iterative developments in science and technology continue to enrich the way people live and produce and raise the demand for aesthetics. The reason for developing artificial intelligence technology is to better serve people themselves, while the integration of art and design is precisely from the emotional needs of people, where art is more prominent in the expression of the subjective concept of people. The future of artificial intelligence is to think about the needs of people and the ability to change different environmental needs. From the current application of artificial intelligence scenarios, artificial intelligence in solving certain boundary problems, people are bound to lose to the machine; however, the need to make decisions with the help of emotional judgment, the logical reasoning made based on artificial intelligence big data also exists uncertainty. At present, artificial intelligence can only provide high technology, while the formation of excellent artworks must require new technical means to achieve. People have emphasized that art is the expression of human subjective concepts, just like saying that if it is similar, it is vulgar, and if it is not, it is not. Art does not have boundaries; therefore, artificial intelligence can never replace a human to complete subjective concepts. The key to the integration of artificial intelligence technology and the art of combining digital and multimedia is to obtain new methods of art created with the help of artificial intelligence technology, so as to better enrich the means of the art design, so the core of the integration of the two depends on people’s values and cognitive ability of artificial intelligence [22].In summary, we know the concept of artificial intelligence, the way of integration of artificial intelligence and the art of combining digital and multimedia and the core significance. In the context of artificial intelligence, intelligent machines can drive the continuous development and progress of society by virtue of their powerful computing power, and the next stage of deeper integration and innovation of artificial intelligence and the art of combining digital and multimedia will definitely present the following development characteristics. The art of combining digital and multimedia has characteristics such as openness, integration, and interactivity, and there are also artistic time-sensitive features that change with time or forms, and it is based on these special features that make the current art of combining digital and multimedia more and more diversified. In recent years, the rapid development of computer and network technology in China has reached the needs of art creation making the organic integration of art and artificial intelligence technology. Only by scientifically and rationally utilizing this fusion can the art of combining digital and multimedia give audiences a more perfect visual experience and strengthen their perceptions and impressions of the works of art creation. With various high-tech as the creative support of the art of combining digital and multimedia, it will let art and technology burst into more artistic miracles. Actively expanding the diversity of digital media, the characteristics of the art of combining digital and multimedia are more diverse, yet in fact, the development of the art of combining digital and multimedia and artificial intelligence technology shows an obvious sense of weakness. In this period of sustainable development of the art of combining digital and multimedia and simultaneous technological progress, Chinese art of combining digital and multimedia will present more new types of art in a richer form of expression, prompting digital media to achieve diverse development, bringing audiences a completely different consumer experience and a new experience and artistic perceptiveness in visual perception [23–25]. From this point of view, the mutual integration of the art of combining digital and multimedia and artificial intelligence technology will certainly make the development space of art and technology more extensive. From the analysis of the development history of the art of combining digital and multimedia in the past, the integration of the art of combining digital and multimedia and artificial intelligence technology in China can only be better presented in the form of technology and art through digital media by continuously strengthening its own traditional cultural heritage and modern civilization. Most people one-sidedly believe that digital technology and computer technology is the catalyst for the development of the art of combining digital and multimedia and technology, however, in fact from another perspective, it is based on the background of the artificial intelligence era that society has stepped into a new period of development of art and technology. From the current point of view, Chinese art of combining digital and multimedia is bound to remain in the early stages of development for a long time and to face many difficulties and problems. In the context of the artificial intelligence era, there will also be a large number of art and design talents emerging for continuous research and exploration, and the emergence of these talents will certainly lead to further development and enhancement of the art of combining digital and multimedia and artificial intelligence technology. In this process, it is especially worth noting that the excellent Chinese cultural connotations should not be abandoned. Commitment to digging out more beneficial art of combining digital and multimedia elements from traditional culture is the only way to make the art of combining digital and multimedia always develop in a more meaningful way.


## 3. Methods

### 3.1. Model Architecture

The art of combining digital and multimedia style conversion is an important technique for nonrealistic drawing in computer graphics. For texture synthesis, existing nonparametric algorithms can synthesize realistic natural textures by resampling the pixels of a given source texture. Most previous texture transfer algorithms use these nonparametric methods for texture synthesis, while using different methods to preserve the structure of the target image. This study uses a deep convolutional neural network to learn a generic feature representation that performs texture transfer (i.e., style transformation) while preserving the semantic content of the target image. The model structure of this paper is shown in Figure2.Figure 2
Model structure.


### 3.2. Image Semantic Content Representation

Given an input imagex, each layer of the convolutional neural network is encoded in each layer using the image’s filter. A layer of the neural network contains Nl different filters, i.e., it has Nl feature mappings, each of size Ml. Therefore, the response in layer l can be stored in the matrix Fl, where Fijl is the activation function of the ith filter at position j in layer l. To visualize the image information encoded on different layers, gradient descent can be performed on the white noise image to find another image that matches the feature response of the original image. Let p and x be the original image and the generated image, and let Pl and Fl represent the features in the lth layer, respectively. Then, define the squared error loss between the two feature representations as follows:(1)Lcontentp,x⟶,l=12∑i,jFijl−Pijl2.With respect to the activation function in layerl, the derivative of the loss function as(2)dLcontentdFijl=Fl−Plij,ifFijl>0,0,ifFijl<0.The standard error backpropagation can then be used to calculate the gradient with respect to the imagex. Thus, the initial random image x can be changed until it generates the same response in a particular layer of the convolutional neural network as the original image p. The schematic diagram of semantic content representation and transformation is shown in Figure 3.Figure 3
Schematic diagram of semantic content representation and transformation.


### 3.3. Artistic Style Representation

The generative network model in the art of combining digital and multimedia style network uses a residual network, which uses a “bootstrap” approach to effectively solve the problem of gradient disappearance when the deep learning network is too deep. In addition, the advantage of using the residual layer to build the network is that the residual layer has a faster training speed than the general convolutional layer with the same convolutional effect. The style transition diagram is shown in Figure4. The perceptual network model uses a five-layer residual network for image feature extraction; however, the perceptual network does not have a deep depth, with three convolutional layers, five residual layers plus three deconvolutional layers for a total of eleven layers. Each residual layer contains two convolutional layers and the size of the convolutional kernel is 3 × 3. The art of combining digital and multimedia generation network is deepened and transformed sufficiently based on this, and ten residual layers are chosen for the middle part of the art of combining digital and multimedia generation network to play the function of the residual network as much as possible under the condition of the existing experimental environment. At the same time, the residual layer of the art of combining digital and multimedia generation network is adjusted from two 3 × 3 convolutional kernels to a combination of two 1 × 1 convolutional kernels and one 3 × 3 convolutional kernel. 1 × 1 convolutional kernels are not useful in two-dimensional plane operations, but each network layer of the art of combining digital and multimedia generation network has multiple channels for receiving image data and its response, and each network layer has multiple convolutional kernels. At this point, the 1 × 1 convolutional kernel can downscale these stacked 3D data so that the number of input and output channels of the convolutional layer is reduced, thus reducing the number of network parameters, and the training speed of the network model is significantly improved. Considered from the opposite perspective, due to the use of 1 × 1 convolutional kernels, the art of combining digital and multimedia generation network can be allowed to receive image data with larger dimensions at the same network size. For example, the original art of combining digital and multimedia generation network allows input images with a maximum dimension of 512 × 512, while the improved network can accommodate images with a resolution of 2048 × 2048. The selection of better quality and larger resolution images will result in better visualization of the experimental results. These feature correlations are represented by the Gram matrix Gijl, where Gijl is the inner product between vectorized feature mappings i and j in layer l.(3)Gijl=∑kFiklFjkl.Figure 4
Schematic diagram of style transformation.Leta and x be the original image and the generated image, while Aijl and Gijl denote the styles of the original and generated image layers l, respectively. Then, the loss of layer l can be expressed as(4)El=14Nl2Ml2∑i,jGijl−Aijl2.The total loss function has the following formula:(5)Lstylea,x⟶=∑lwlEl,where wl is the weighting factor. With respect to the activation function in layer l, the derivative of El can be expressed as(6)∂El∂Fijl=1Nl2Ml2FlTGl−Alji,ifFijl>0,0,ifFijl<0,The gradient ofEl with respect to the pixel value x can be easily calculated using standard error back propagation.


### 3.4. Training the Art of Combining Digital and Multimedia Style Network

The art of combining digital and multimedia style network is constructed, and similar to other CNN networks, the network needs to rely on a large amount of image data and loss functions for training. In this paper, we use the MSCOCO2014 dataset, which contains 328,000 images and is divided into 91 categories. The update of the art of combining digital and multimedia generation network parameters relies on the loss function and the stochastic gradient descent algorithm, and the specific process of training is as follows: firstly, the noisy imagex is input into the art of combining digital and multimedia generation network, and x is convolved and deconvoluted by the network operation to obtain images of the same size as x. The content map y and a specified art of combining digital and multimedia image y in the MSCOCO dataset are used in the art of combining digital and multimedia discriminative, the calculation results are fed back to the art of combining digital and multimedia generation network using the BP algorithm, and the parameters and weights of each layer of the art of combining digital and multimedia generation network are updated to minimize the total loss cost. Since the dataset provided to the art of combining digital and multimedia generation network for training is very large, the network has a good generalization ability after the training is completed, and it is able to apply the style of the art of combining digital and multimedia to any unknown picture. In this paper, content loss function ℓfeatϕ,jy^,yc and style loss function ℓstyleϕ,jy^,ys are constructed for picture content and the art of combining digital and multimedia style, respectively, and the content loss function calculates and y. The square of the Euclidean distance between the response y^ and yc obtained at a layer j after input pretraining the model, where CjHjWj refers to the data obtained at layer j after computing the size and dimensionality of the feature mapping.(7)ℓfeatϕ,jy^,yc=1CjHjWjϕjy^−ϕjyc22.The extraction of image styles relies on the Gram matrix, Gram matrixGjϕxc,c′ calculates the inner product between the same dimensional feature mapping ϕjxh,w,c and ϕjxh,w,c′ obtained at layer j. The covariance operation in the Gram matrix can be used to understand the covariance relationship between different feature mapping of responses in the same layer in the network, and also to know which network nodes are activated, responsive and cooperative at the same time. The style loss function is defined as the F-parametric of the difference between Gjϕy^ and Gjϕys.(8)Gjϕxc,c′=1cjHjWj∑h=1Hj∑w=1Wjϕjxh,w,cϕjxh,w,c′,ℓstyleϕ,jy^,ys=1cjHjWjGjϕy^−GjϕysF2.Besides, this paper uses the full variational regularizationλTVℒTVy^ to ensure the smoothness of the final generated images. The weighted sum of the content loss function λcℓfeatϕ,jy^,y, the style loss function ℓstyleϕ,jy^,y, and the full-variance regularization ℒTVy^ yields the final global loss function.(9)ℒtotal=argminyλcℓfeatϕ,jy^,y+λsℓstyleϕ,jy^,y+λTVℒTVy^.The training goal of the art of combining digital and multimedia generation network is to minimize the global loss function and use this value to update the response while using the BP algorithm back to the neurons of the art of combining digital and multimedia generation network, which use the gradient descent algorithm to update the parameter weights. The whole process of training the art of combining digital and multimedia generation network can also be regarded as using the feature extraction capability of the VGG-19 model parameters to help the art of combining digital and multimedia generation network obtain the corresponding art of combining digital and multimedia style simulation parameters based on the existing VGG-19 pretraining model. The trained art of combining digital and multimedia generation network has a strong generalization ability and can map the art of combining digital and multimedia style to any unknown image. Since the parameters of the generative network are mature enough, the next image that needs to map the art of combining digital and multimedia style does not need to calculate the loss function, and a forward propagation operation can be performed directly in the art of combining digital and multimedia generative network, so the art of combining digital and multimedia generative network is very fast in style mapping.


## 3.1. Model Architecture

The art of combining digital and multimedia style conversion is an important technique for nonrealistic drawing in computer graphics. For texture synthesis, existing nonparametric algorithms can synthesize realistic natural textures by resampling the pixels of a given source texture. Most previous texture transfer algorithms use these nonparametric methods for texture synthesis, while using different methods to preserve the structure of the target image. This study uses a deep convolutional neural network to learn a generic feature representation that performs texture transfer (i.e., style transformation) while preserving the semantic content of the target image. The model structure of this paper is shown in Figure2.Figure 2
Model structure.


## 3.2. Image Semantic Content Representation

Given an input imagex, each layer of the convolutional neural network is encoded in each layer using the image’s filter. A layer of the neural network contains Nl different filters, i.e., it has Nl feature mappings, each of size Ml. Therefore, the response in layer l can be stored in the matrix Fl, where Fijl is the activation function of the ith filter at position j in layer l. To visualize the image information encoded on different layers, gradient descent can be performed on the white noise image to find another image that matches the feature response of the original image. Let p and x be the original image and the generated image, and let Pl and Fl represent the features in the lth layer, respectively. Then, define the squared error loss between the two feature representations as follows:(1)Lcontentp,x⟶,l=12∑i,jFijl−Pijl2.With respect to the activation function in layerl, the derivative of the loss function as(2)dLcontentdFijl=Fl−Plij,ifFijl>0,0,ifFijl<0.The standard error backpropagation can then be used to calculate the gradient with respect to the imagex. Thus, the initial random image x can be changed until it generates the same response in a particular layer of the convolutional neural network as the original image p. The schematic diagram of semantic content representation and transformation is shown in Figure 3.Figure 3
Schematic diagram of semantic content representation and transformation.


## 3.3. Artistic Style Representation

The generative network model in the art of combining digital and multimedia style network uses a residual network, which uses a “bootstrap” approach to effectively solve the problem of gradient disappearance when the deep learning network is too deep. In addition, the advantage of using the residual layer to build the network is that the residual layer has a faster training speed than the general convolutional layer with the same convolutional effect. The style transition diagram is shown in Figure4. The perceptual network model uses a five-layer residual network for image feature extraction; however, the perceptual network does not have a deep depth, with three convolutional layers, five residual layers plus three deconvolutional layers for a total of eleven layers. Each residual layer contains two convolutional layers and the size of the convolutional kernel is 3 × 3. The art of combining digital and multimedia generation network is deepened and transformed sufficiently based on this, and ten residual layers are chosen for the middle part of the art of combining digital and multimedia generation network to play the function of the residual network as much as possible under the condition of the existing experimental environment. At the same time, the residual layer of the art of combining digital and multimedia generation network is adjusted from two 3 × 3 convolutional kernels to a combination of two 1 × 1 convolutional kernels and one 3 × 3 convolutional kernel. 1 × 1 convolutional kernels are not useful in two-dimensional plane operations, but each network layer of the art of combining digital and multimedia generation network has multiple channels for receiving image data and its response, and each network layer has multiple convolutional kernels. At this point, the 1 × 1 convolutional kernel can downscale these stacked 3D data so that the number of input and output channels of the convolutional layer is reduced, thus reducing the number of network parameters, and the training speed of the network model is significantly improved. Considered from the opposite perspective, due to the use of 1 × 1 convolutional kernels, the art of combining digital and multimedia generation network can be allowed to receive image data with larger dimensions at the same network size. For example, the original art of combining digital and multimedia generation network allows input images with a maximum dimension of 512 × 512, while the improved network can accommodate images with a resolution of 2048 × 2048. The selection of better quality and larger resolution images will result in better visualization of the experimental results. These feature correlations are represented by the Gram matrix Gijl, where Gijl is the inner product between vectorized feature mappings i and j in layer l.(3)Gijl=∑kFiklFjkl.Figure 4
Schematic diagram of style transformation.Leta and x be the original image and the generated image, while Aijl and Gijl denote the styles of the original and generated image layers l, respectively. Then, the loss of layer l can be expressed as(4)El=14Nl2Ml2∑i,jGijl−Aijl2.The total loss function has the following formula:(5)Lstylea,x⟶=∑lwlEl,where wl is the weighting factor. With respect to the activation function in layer l, the derivative of El can be expressed as(6)∂El∂Fijl=1Nl2Ml2FlTGl−Alji,ifFijl>0,0,ifFijl<0,The gradient ofEl with respect to the pixel value x can be easily calculated using standard error back propagation.


## 3.4. Training the Art of Combining Digital and Multimedia Style Network

The art of combining digital and multimedia style network is constructed, and similar to other CNN networks, the network needs to rely on a large amount of image data and loss functions for training. In this paper, we use the MSCOCO2014 dataset, which contains 328,000 images and is divided into 91 categories. The update of the art of combining digital and multimedia generation network parameters relies on the loss function and the stochastic gradient descent algorithm, and the specific process of training is as follows: firstly, the noisy imagex is input into the art of combining digital and multimedia generation network, and x is convolved and deconvoluted by the network operation to obtain images of the same size as x. The content map y and a specified art of combining digital and multimedia image y in the MSCOCO dataset are used in the art of combining digital and multimedia discriminative, the calculation results are fed back to the art of combining digital and multimedia generation network using the BP algorithm, and the parameters and weights of each layer of the art of combining digital and multimedia generation network are updated to minimize the total loss cost. Since the dataset provided to the art of combining digital and multimedia generation network for training is very large, the network has a good generalization ability after the training is completed, and it is able to apply the style of the art of combining digital and multimedia to any unknown picture. In this paper, content loss function ℓfeatϕ,jy^,yc and style loss function ℓstyleϕ,jy^,ys are constructed for picture content and the art of combining digital and multimedia style, respectively, and the content loss function calculates and y. The square of the Euclidean distance between the response y^ and yc obtained at a layer j after input pretraining the model, where CjHjWj refers to the data obtained at layer j after computing the size and dimensionality of the feature mapping.(7)ℓfeatϕ,jy^,yc=1CjHjWjϕjy^−ϕjyc22.The extraction of image styles relies on the Gram matrix, Gram matrixGjϕxc,c′ calculates the inner product between the same dimensional feature mapping ϕjxh,w,c and ϕjxh,w,c′ obtained at layer j. The covariance operation in the Gram matrix can be used to understand the covariance relationship between different feature mapping of responses in the same layer in the network, and also to know which network nodes are activated, responsive and cooperative at the same time. The style loss function is defined as the F-parametric of the difference between Gjϕy^ and Gjϕys.(8)Gjϕxc,c′=1cjHjWj∑h=1Hj∑w=1Wjϕjxh,w,cϕjxh,w,c′,ℓstyleϕ,jy^,ys=1cjHjWjGjϕy^−GjϕysF2.Besides, this paper uses the full variational regularizationλTVℒTVy^ to ensure the smoothness of the final generated images. The weighted sum of the content loss function λcℓfeatϕ,jy^,y, the style loss function ℓstyleϕ,jy^,y, and the full-variance regularization ℒTVy^ yields the final global loss function.(9)ℒtotal=argminyλcℓfeatϕ,jy^,y+λsℓstyleϕ,jy^,y+λTVℒTVy^.The training goal of the art of combining digital and multimedia generation network is to minimize the global loss function and use this value to update the response while using the BP algorithm back to the neurons of the art of combining digital and multimedia generation network, which use the gradient descent algorithm to update the parameter weights. The whole process of training the art of combining digital and multimedia generation network can also be regarded as using the feature extraction capability of the VGG-19 model parameters to help the art of combining digital and multimedia generation network obtain the corresponding art of combining digital and multimedia style simulation parameters based on the existing VGG-19 pretraining model. The trained art of combining digital and multimedia generation network has a strong generalization ability and can map the art of combining digital and multimedia style to any unknown image. Since the parameters of the generative network are mature enough, the next image that needs to map the art of combining digital and multimedia style does not need to calculate the loss function, and a forward propagation operation can be performed directly in the art of combining digital and multimedia generative network, so the art of combining digital and multimedia generative network is very fast in style mapping.


## 4. Experiments and Results

### 4.1. Experimental Setup

In this paper, we trained and used the art of combining digital and multimedia style network model on a computer with Ubuntu 16.04, GTX1070 graphics card, 8 GB RAM, and TensorFlow as the experimental framework are continuously optimized. This chapter will focus on showing the generated images using different art of combining digital and multimedia style models for stylized mapping, where the art of combining digital and multimedia style network models include the art of combining digital and multimedia models of the slate category with rough textures and bright colors and the art of combining digital and multimedia models of the paper category with delicate brush strokes and a bias toward realism, and the content images include five categories such as people, architecture, landscape, objects, and animals. These perspectives were used to view the degree of similarity between the generated images of the art of combining digital and multimedia style network and real the art of combining digital and multimedia. A total of 25 art of combining digital and multimedia style network models were trained for comparative analysis with many different categories of real the art of combining digital and multimedia. The real art of combining digital and multimedia can be broadly classified into board the art of combining digital and multimedia and paper the art of combining digital and multimedia according to the drawing method and visual effect. Before training the network models, different training parameters were set based on the different styles of real the art of combining digital and multimedia. One of the main parameters is the ratio of content weights to style weights in the loss function, i.e., the ratio of the weights of the content loss function to the weights of the style loss functionα/β, the higher the ratio, the less stylized the generated images are, and vice versa. For the board-like the art of combining digital and multimedia network model, the value of α/β should be set larger in order to retain the roughness of lines and the visual effect of color interweaving in the real board-like the art of combining digital and multimedia as much as possible, and 0.15 is chosen as the final parameter in this paper. The value of α/β should be smaller, and 0.3 is chosen as the final parameter. The training process loss convergence curve and performance improvement are shown in Figures 5 and 6.Figure 5
Training process loss convergence curve.Figure 6
Training process performance improvement diagram.


### 4.2. Experimental Results

Using the network model for the experiments on the original data and the data after data enhancement, the results are shown in Table1, and it can be seen that the classification accuracy after data enhancement is improved by 9.21%.Table 1
Classification results before and after data enhancement.
Image data processingAccuracy rateWithout data enhancement77.34With data enhancement86.55Comparison of the results of different classification methods: comparison with the traditional network model. In order to verify the effectiveness of this paper’s network model for classifying the art of combining digital and multimedia images, the data of this paper were input into the traditional network model and this paper’s network model for training and verification, and the results are shown in Figures7 and 8.Figure 7
Comparison of classification time of different network models.Figure 8
Comparison of the classification results of different network models.From Figures7 and 8, compared with the existed network model, the network model in this paper is lightweight, has short training time, and has the best accuracy rate. However, the proposed network module contains parallel convolution operations, which makes the parameters of the network model in this paper higher than those of Shuffle-Net, MobileNetV1, and MobileNetV2. The method + SE and the method + SK in this paper are network models that replace the proposed modules with the SE and SK modules, and the values of the descent rate r in the modules are taken as 16. The experimental results show that the accuracy of the network model in this paper is 0.86% and 1.25% higher than that of the method + SE and the method + SK in this paper, respectively. When the r value of the proposed module is taken as 4, the classification accuracy is higher than that when r is taken as 16.Comparing with the traditional methods, it can be seen from Table2 that the traditional manually extracted underlying overall features based on color, shape, etc., and local features cannot fully distinguish the style features of various types of art images, while the method in this paper can better extract the overall features and local detail features of art images and improve the classification accuracy.Table 2
Comparison of the results of the method in this paper with the traditional method.
MethodsAccuracy rate (%)Comparison method 166.78Comparison method 260.2Method of this paper86.55Module role: location is to see the effect of the proposed module on the classification of art images at different locations of the network model, it is placed separately at the locations of the network model numbered as 6, 9, and 12 in this paper, and the convolutional kernels of 3 × 3 and 5 × 5 are taken on the branches, respectively, and the descent rater value is taken as 4. The results are shown in Figures 9 and 10.Figure 9
Accuracy of the module in different positions.Figure 10
Operation volume of the module in different positions.From Figures9 and 10, the proposed module has the highest classification accuracy and the least computational consumption for the art images when it is placed at the position of network model number 6 alone.The drop rater and the branch convolution kernel size are an important set of parameters in the proposed module for controlling the computational resources and experimental accuracy. The proposed module is individually placed in the network model with numbering position 6, and the proposed r value and the branch convolution kernel size are experimented and analyzed, and the results are shown in Table 3. It can be seen that when the size of the convolution kernel on the branch of the proposed module is fixed, the classification results are higher when the r value is taken as 4 than when the r value is taken as 16; when the r value is fixed, the proposed module of 2 branches takes less time and fewer parameters than the training of 3 branches; when the convolution kernel on the branch of the proposed module is taken as 1 × 1 and 5 × 5, respectively, the experimental results have the highest accuracy rate.Table 3
Comparison of drop rate and convolution kernel size classification results.
Decline rate1 × 13 × 35 × 5Parameter (M)Time (min)Accuracy rate (%)R = 4✓✓2.3136787.26✓✓2.6168087.58✓✓2.7180087.24✓✓✓2.7237087.35R = 16✓✓2.3123086.35✓✓2.6122086.85✓✓2.7144086.15✓✓✓2.7177086.42Null convolution: in order to compare the effect of null convolution kernels on art image feature extraction, different sizes of null convolution kernels are taken on the proposed module for experiments on the data of this paper, and the results are shown in Table4, where K3 denotes an ordinary 3 × 3 convolution kernel, K5 denotes a 3 × 3 convolution kernel with a null convolution expansion rate of 2 and a perceptual field of 5 × 5, and K7 denotes a 3 × 3 convolution kernel with an expansion rate of 3 and a perceptual field of 7 × 7.Table 4
Module branching takes the classification results of different null convolution kernels.
Decline rateK = 3K = 5K = 7Parameter (M)Time (min)Accuracy rate (%)R = 4✓✓2.4153086.07✓✓2.4165786.00✓✓2.4156084.50✓✓✓2.6222086.40The experimental results show that the parameters of the null convolution are less than those of the normal convolution with the same receptive field, but the classification accuracy is not as high as that of the normal convolution. The 3 × 3 convolutional kernels with 5 × 5 receptive fields andK7 indicates the 3 × 3 convolutional kernel with 3 expansion rate and 7 × 7 receptive fields.


## 4.1. Experimental Setup

In this paper, we trained and used the art of combining digital and multimedia style network model on a computer with Ubuntu 16.04, GTX1070 graphics card, 8 GB RAM, and TensorFlow as the experimental framework are continuously optimized. This chapter will focus on showing the generated images using different art of combining digital and multimedia style models for stylized mapping, where the art of combining digital and multimedia style network models include the art of combining digital and multimedia models of the slate category with rough textures and bright colors and the art of combining digital and multimedia models of the paper category with delicate brush strokes and a bias toward realism, and the content images include five categories such as people, architecture, landscape, objects, and animals. These perspectives were used to view the degree of similarity between the generated images of the art of combining digital and multimedia style network and real the art of combining digital and multimedia. A total of 25 art of combining digital and multimedia style network models were trained for comparative analysis with many different categories of real the art of combining digital and multimedia. The real art of combining digital and multimedia can be broadly classified into board the art of combining digital and multimedia and paper the art of combining digital and multimedia according to the drawing method and visual effect. Before training the network models, different training parameters were set based on the different styles of real the art of combining digital and multimedia. One of the main parameters is the ratio of content weights to style weights in the loss function, i.e., the ratio of the weights of the content loss function to the weights of the style loss functionα/β, the higher the ratio, the less stylized the generated images are, and vice versa. For the board-like the art of combining digital and multimedia network model, the value of α/β should be set larger in order to retain the roughness of lines and the visual effect of color interweaving in the real board-like the art of combining digital and multimedia as much as possible, and 0.15 is chosen as the final parameter in this paper. The value of α/β should be smaller, and 0.3 is chosen as the final parameter. The training process loss convergence curve and performance improvement are shown in Figures 5 and 6.Figure 5
Training process loss convergence curve.Figure 6
Training process performance improvement diagram.


## 4.2. Experimental Results

Using the network model for the experiments on the original data and the data after data enhancement, the results are shown in Table1, and it can be seen that the classification accuracy after data enhancement is improved by 9.21%.Table 1
Classification results before and after data enhancement.
Image data processingAccuracy rateWithout data enhancement77.34With data enhancement86.55Comparison of the results of different classification methods: comparison with the traditional network model. In order to verify the effectiveness of this paper’s network model for classifying the art of combining digital and multimedia images, the data of this paper were input into the traditional network model and this paper’s network model for training and verification, and the results are shown in Figures7 and 8.Figure 7
Comparison of classification time of different network models.Figure 8
Comparison of the classification results of different network models.From Figures7 and 8, compared with the existed network model, the network model in this paper is lightweight, has short training time, and has the best accuracy rate. However, the proposed network module contains parallel convolution operations, which makes the parameters of the network model in this paper higher than those of Shuffle-Net, MobileNetV1, and MobileNetV2. The method + SE and the method + SK in this paper are network models that replace the proposed modules with the SE and SK modules, and the values of the descent rate r in the modules are taken as 16. The experimental results show that the accuracy of the network model in this paper is 0.86% and 1.25% higher than that of the method + SE and the method + SK in this paper, respectively. When the r value of the proposed module is taken as 4, the classification accuracy is higher than that when r is taken as 16.Comparing with the traditional methods, it can be seen from Table2 that the traditional manually extracted underlying overall features based on color, shape, etc., and local features cannot fully distinguish the style features of various types of art images, while the method in this paper can better extract the overall features and local detail features of art images and improve the classification accuracy.Table 2
Comparison of the results of the method in this paper with the traditional method.
MethodsAccuracy rate (%)Comparison method 166.78Comparison method 260.2Method of this paper86.55Module role: location is to see the effect of the proposed module on the classification of art images at different locations of the network model, it is placed separately at the locations of the network model numbered as 6, 9, and 12 in this paper, and the convolutional kernels of 3 × 3 and 5 × 5 are taken on the branches, respectively, and the descent rater value is taken as 4. The results are shown in Figures 9 and 10.Figure 9
Accuracy of the module in different positions.Figure 10
Operation volume of the module in different positions.From Figures9 and 10, the proposed module has the highest classification accuracy and the least computational consumption for the art images when it is placed at the position of network model number 6 alone.The drop rater and the branch convolution kernel size are an important set of parameters in the proposed module for controlling the computational resources and experimental accuracy. The proposed module is individually placed in the network model with numbering position 6, and the proposed r value and the branch convolution kernel size are experimented and analyzed, and the results are shown in Table 3. It can be seen that when the size of the convolution kernel on the branch of the proposed module is fixed, the classification results are higher when the r value is taken as 4 than when the r value is taken as 16; when the r value is fixed, the proposed module of 2 branches takes less time and fewer parameters than the training of 3 branches; when the convolution kernel on the branch of the proposed module is taken as 1 × 1 and 5 × 5, respectively, the experimental results have the highest accuracy rate.Table 3
Comparison of drop rate and convolution kernel size classification results.
Decline rate1 × 13 × 35 × 5Parameter (M)Time (min)Accuracy rate (%)R = 4✓✓2.3136787.26✓✓2.6168087.58✓✓2.7180087.24✓✓✓2.7237087.35R = 16✓✓2.3123086.35✓✓2.6122086.85✓✓2.7144086.15✓✓✓2.7177086.42Null convolution: in order to compare the effect of null convolution kernels on art image feature extraction, different sizes of null convolution kernels are taken on the proposed module for experiments on the data of this paper, and the results are shown in Table4, where K3 denotes an ordinary 3 × 3 convolution kernel, K5 denotes a 3 × 3 convolution kernel with a null convolution expansion rate of 2 and a perceptual field of 5 × 5, and K7 denotes a 3 × 3 convolution kernel with an expansion rate of 3 and a perceptual field of 7 × 7.Table 4
Module branching takes the classification results of different null convolution kernels.
Decline rateK = 3K = 5K = 7Parameter (M)Time (min)Accuracy rate (%)R = 4✓✓2.4153086.07✓✓2.4165786.00✓✓2.4156084.50✓✓✓2.6222086.40The experimental results show that the parameters of the null convolution are less than those of the normal convolution with the same receptive field, but the classification accuracy is not as high as that of the normal convolution. The 3 × 3 convolutional kernels with 5 × 5 receptive fields andK7 indicates the 3 × 3 convolutional kernel with 3 expansion rate and 7 × 7 receptive fields.


## 5. Conclusion

Along with the accelerated development of artificial intelligence technology, designers in the traditional design field will be gradually outlawed by intelligent machines if they do not review the situation, transform, and improve themselves in time. However, not all the work in the field of art and design can be replaced by it, especially some creative design concepts and contents that no high-tech and machine can do. As a professional, in the field of art and design, we should think more about the intangible value in art and design and only the core competitiveness of design talents is to burst out a constant source of creativity and inventiveness. The powerful computing power of artificial intelligence can assist them to use historical big data to explore human needs comprehensively, help people to solve practical problems through design, and use data to obtain design thinking. At the same time, the current global information is extremely rich and diverse, and this information will drive people to accept new products, new technologies, and new digital life in a passive mode. In-depth exploration of the aesthetics and wisdom of digital media design requires art designers to look at the changing times with their own understanding and feel the difference in the life of the times. This paper mainly proposes a method based on the convolutional neural network to better achieve feature extraction and classification of art images. This paper solves the problem of insufficient research on the classification of existing multi-class art images, and achieves better classification results of art images than existing network models and traditional classification methods. In the subsequent work, we will optimize the art image classification network model, expand the art image sample library, and further improve the accuracy of art image classification and the efficiency of network model classification.



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*Source: 1004204-2022-08-08.xml*