In the context of a bibliometrics on the use of machine learning models to predict artistic styles in paintings, it is crucial to consider and assess the risk of bias derived from the lack of results in a synthesis, which may arise due to reporting biases. In the context of this research on the use of machine learning models to predict artistic styles in paintings, different procedures were followed to decide which studies to include in the bibliometric synthesis. These tools were crucial for the analysis, allowing us to observe emerging patterns and trends in the scientific literature on machine learning models applied to predicting artistic styles in painting. In addition to the implications mentioned above, the bibliometric study on machine learning models for predicting artistic styles also has important implications for the artistic field in terms of democratizing creativity and exploring new artistic horizons. These results allow a better understanding of the dynamics and approaches of the different journals in the field of deep learning applied to the analysis of artistic styles in paintings. These results allow a better understanding of the dynamics and contributions of the different groups of authors in the field of deep learning applied to the analysis of artistic styles in paintings.
Germany has contributed significant research in the field of robotics applied to painting, which is key to address the automation of the painting process and its link with artificial intelligence (Gülzow et al. 2020). Other studies have analyzed these models in other contexts, such as art therapy for children with autism, which highlights the leading role of artificial intelligence in supporting and improving artistic interventions in specific populations (Hu 2022). In 2020, two pivotal studies ventured beyond the traditional confines of art, exploring the application of deep learning in diverse fields.
The Implications for Artists
Instead, the scientific landscape on the topic is analyzed by examining the number of publications and the number of citations related to the topic of interest. However, this review, which is based on secondary research sources, takes a different approach by not directly synthesizing the results of primary studies. It should be noted that this tool provides a systematic and efficient approach to the analysis of the included studies, which strengthens the validity and reliability of the conclusions drawn from this bibliometric work. In accordance with the methodology adopted, all authors were involved in the data collection and the risk of bias was assessed in the same way.
Machine learning identifies anti-aging neuroprotective treatments
Adaptive adversarial neural networks; convolutional neural network; crack detection; deep-learning; segmentation; U-Net; virtual restoration of paintings A method of generating abstract ink paintings based on machine learning Analysis framework; art history; color representations; cultural analysis; dataset; deep learning; information theory; style extraction Assessing the best art design based on artificial intelligence and machine learning using GTMA
Analyzing Artistic Data
Research on authenticity identification of Chinese painting based on computer technology Art attribution; Machine learning; Multi-scale classification; Pyramid of histogram of oriented gradients; Residual neural network Originality; Unsupervised machine learning; Copyright; Design rights; Intellectual property App; augmented reality; culture; machine learning; poetry Adaptive support; exoskeletons; force myography; human–machine interaction; machine learning Artificial intelligence raters; Machine learning; Neural nets; Pictorial expression; Visual art
Philosophical context
These advances have the potential to transform the way artists create and express themselves, as well as enrich the understanding of cultural manifestations over time. Among them, the International Conference on Intelligent User Interfaces (IUI) has been an important pillar in advancing research in human-computer interaction applied to interactive learning. Two pivotal figures, Fails, a primary author in the field, and Olsen, the second most cited, have significantly shaped human-computer interaction. While acknowledging this trend, it’s essential to broaden the scope beyond historical art and incorporate contemporary perspectives.
Convolutional neural networks; deep learning; generative adversarial networks; image re-coloring; self-attention GAN Automatic annotation; deep learning; digitized fine-Art paintings; object detection. Machine learning; Virtual reality technology; Oil painting art; Teaching video analysis
- Automated art dates back at least to the automata of ancient Greek civilization, when inventors such as Daedalus and Hero of Alexandria were described as designing machines capable of writing text, generating sounds, and playing music.
- Chinese painting; computer technology; computer vision technology; identification
- Instead, the scientific landscape on the topic is analyzed by examining the number of publications and the number of citations related to the topic of interest.
- Adaptive support; exoskeletons; force myography; human–machine interaction; machine learning
- The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN to allow users to generate and modify images such as faces, landscapes, and paintings.
- This question arises with the aim of; Determining the primary and influential research works in the domain of machine learning used for predicting artistic styles, emphasizing key references that have significantly impacted this field.
Understanding AI’s Predictive Power
While AI has shown promising capabilities in predicting artistic trends, there are limitations to its predictive power. One of the main challenges in predicting artistic trends is identifying emerging patterns that signify a shift in artistic direction. In this article, we will explore the capabilities of AI https://xolivi.com/en-in/ in predicting artistic trends and its potential impact on the art world. We find this for other types of images too – memorability isn’t well predicted by similar types of features for faces, or for real-world objects,” she said.
Artistic Media Stylization and Identification Using Convolution Neural Networks
In another study by Tzeng et al. (2005), the authors introduced a new approach to the volume classification problem that combined machine learning and a painting metaphor to allow more sophisticated classification in an intuitive manner. Furthermore, there is a need for additional research on the interpretation and explanation of features learned by machine learning models employed for this task. In a broader context, Lürig et al. (2021) underscore the potential of computer vision and machine learning across disciplines like ecology and evolutionary biology, where predictive models have propelled significant advances in phenomics. Other endeavors have explored the development of a catalog raisonné using artificial intelligence and machine learning (Dobbs et al. 2022).
Imagery
The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. Evolution of entropy in art painting based on the wavelet transform Virtual restoration of paintings using adaptive adversarial neural network AI visual content creation; Contrastive learning; Text-to-image model Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data Abstract ink painting; Generative art; Image generation; Machine learning
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- For example, Li and Chen (2009) tackled this challenge as a machine learning problem, aiming to evaluate the aesthetic quality of paintings based on their visual content.
- Institutions are increasingly recognizing the historical marginalization of Indigenous artists and the need to honor their contributions through dedicated exhibitions, acquisitions, and programming.
- For example, an AI algorithm might identify a sudden surge in popularity for a particular art style or theme across different platforms and locations.
- For example, maybe you hate the look of the Old Dutch Masters paintings, but your grandmother had a print of “Girl with a Pearl Earring” hanging in her living room.
Moreover, another work conducted in 2020 proposed a method to integrate an artistic style into brushstrokes and the painting process through the collaboration of robotic painting with a human artist. The authors applied this method to a small collection of paintings apparently by Nicholas Poussin and a small collection of paintings by Vincent van Gogh, emphasizing the enduring relevance of historical artworks in the contemporary analytical landscape. However, the author generalized this state of the art and did not delve specifically and deeply into its relation with art, particularly painting.
Ranking the art design and applications of artificial intelligence and machine learning Mining exoticism from visual content with fusion-based deep neural networks A two-stream neural network architecture for the detection and analysis of cracks in panel paintings Artificial intelligence; Computational art analysis; Computer-assisted connoisseurship; Deep neural networks; Religious symbols and attributes; Semantic image analysis; Visual semiotics Predicting damage evolution in panel paintings with machine learning Inferring compositional style in the neo-plastic paintings of Piet Mondrian by machine learning
Quantifying Visual Similarity for Artistic Styles
Second, articles containing a combination of the terms “machine learning” and terms beginning with “painting” are included. Another interesting contribution was made by Mengyao and Yu (2023), who conducted a trend analysis in product art design, primarily focusing on industrial product design using machine learning. Subsequently, using the artwork appreciation dataset, they proposed a convolutional neural network model based on AlexNet to utilize the powerful feature extraction and classification capabilities of neural networks to complete the appreciation of artworks. They created an artwork appreciation dataset consisting of fifty Chinese paintings and fifty Western oil paintings, recruiting twenty subjects to rate the art appreciation of a hundred artworks in the dataset, encompassing both aesthetic evaluation and emotion evaluation of the painting. In 2021, some authors proposed using a convolutional graph network and artistic comments instead of paint color to classify the type, school, time period, and author of paintings by implementing natural language processing (NLP) techniques (Zhao et al. 2021).
Humanist-in-the-Loop: Machine Learning and the Analysis of Style in the Visual Arts
Image captioning on fine art paintings via virtual paintings Classification of Chinese paintings; Deep learning; Embedded learning; Mutual information Big data; Machine learning; Master data; Production management; Shipbuilding; Statistical analysis Activity classification; Construction workers; Productivity analysis; Supervised machine learning; Wearable accelerometers Detection of forgery in paintings using supervised learning
Analyzing Artistic Data
In 2019, Dinkins won the Creative Capital award for her creation of an evolving artificial intelligence based on the “interests and culture(s) of people of color.” AARON uses a symbolic rule-based approach to generate technical images in the era of GOFAI programming, and it was developed by Cohen with the goal of being able to code the act of drawing. Since the founding of AI in the 1950s, artists have used artificial intelligence to create artistic works. The field of artificial intelligence was founded in the 1950s, and artists began to create art with artificial intelligence shortly after the discipline’s founding.
The research provides valuable insight into how cutting-edge technologies such as artificial intelligence can enrich and enhance the field of artistic design (Wenjing and Cai 2023). Several studios explored this topic in their work, where they evaluated optimal art design using artificial intelligence and machine learning. This cluster is related to the quantification of visual similarity for artistic styles, as discussed in the work of (Sánchez Santana and Roman-Rangel 2021). This approach has proven valuable in the restoration of damaged artistic images, which has led to increased recognition of the practical applications of GANs in the preservation of artistic heritage (Jboor et al. 2019).
Expert Guide on AI and Machine Learning: Comprehensive Course Insights
Independently, the researchers used this tool in the inclusion and exclusion phases of the study to reduce the risk of losing relevant research or incorrect classifications when converging the results obtained. Subsequent literature searches beyond this date may yield a greater volume of information, reflecting the evolving landscape of research in the field. To carry out the bibliometric search in the two selected databases, two highly specialized search equations were developed, adapted to the previously defined inclusion criteria and the specific characteristics of each database. Scopus and Web of Science offer a wide coverage of academic publications from different disciplines, which guarantees the inclusion of a large number of articles related to the research topic in question. Furthermore, the chronological aspect is comprehensively addressed, incorporating articles from all years for which information is available, aligning with the established criteria.
Some studies tackled the generation of image descriptions for artistic paintings using virtual images, advancing our understanding and prediction of artistic styles. Similarly, the use of machine learning algorithms and models in predicting artistic styles can foster the generation of hybrid works that combine human creativity and machine learning capabilities, resulting in unique and surprising artistic expressions. This could indicate a convergence of knowledge and research approaches in certain areas, which could be beneficial for the development of more sophisticated and accurate techniques for predicting artistic styles in paintings. His approach has been recognized for its contribution to the prediction and evaluation of artistic styles in paintings (Li and Chen 2009). This not only aids in machine learning models’ meticulous analysis and description of visual characteristics but also contributes to a more comprehensive understanding of art history and theory, bridging the historical and contemporary facets of art (Lu et al. 2021). In this “Discussion” Section, we provide a detailed analysis of the results of the research on the use of machine learning for predicting artistic styles.
Finally, those documents related to the field of health are excluded. First, the metadata of the title and abstract are considered as essential elements for the selection of the records. In order to achieve the objective of the research, a bibliometric analysis is proposed. A few of the articles included in this literature review, comprising the most cited articles from the 2000 to 2010 s and the most recent ones in the 2020s, are summarized below, as depicted in Table 1. They collected brushstrokes and manual brush movement samples from an artist, then trained a gen-erative model to generate brushstrokes belonging to the artist’s style. The researchers asked annotators to indicate the dominant emotion https://lopesezorzo.com/en-in/ they felt for a specific image and, more importantly, to provide a well-founded verbal ex-planation for their emotion.
In addition, the main research gaps identified during the study are highlighted, indicating opportunities for future research in this area. The limitations of the study are also examined, identifying possible areas for improvement and limitations that could affect the results. In quadrant 4, identified as the quadrant of declining concepts, no relevant keywords were found in this particular case.
Bibliometrics applied to the study of machine learning models for the prediction of artistic styles in paintings has revealed significant practical implications for the scientific community and art professionals. In quadrant 2 of the Cartesian plane of the current bibliometrics on the use of machine learning models to predict artistic styles in paintings, emerging concepts were found that play a crucial role in the current and near future scientific field. This automated approach made it possible to efficiently analyze and quantify key aspects of scientific production related to the study of machine learning models for predicting artistic styles in paintings. Research on the utilization of machine learning models for predicting artistic styles in paintings reveals certain gaps, warranting bibliometric analysis to address and scrutinize the current state of scientific literature in this domain. The application of artificial intelligence in deep learning models has been a significant advance in the prediction of artistic styles in paintings. These emerging concepts, such as “art design,” “supervised machine learning,” and “art history,” have proven to be fundamental in research on predicting artistic styles in paintings using deep learning models.
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