Artificial Neural Networks and Deep Learning in the Visual Arts: a review

Santos, Iria; Castro, Luz; Rodríguez-Fernández, Nereida; Torrente-Patiño, Alvaro; Carballal, Adrián

doi:10.1007/s00521-020-05565-4

Cited by 72 publications

(29 citation statements)

References 180 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vector a is obtained by connecting each x to the starting point [13]. In order to prevent erroneous evaluation results due to data interference, a certain time model is used as the evaluation criterion [14,15].…”

Section: Evolution Wall Painting Patternmentioning

confidence: 99%

RETRACTED ARTICLE: Innovative design of wall painting pattern based on microprocessor system and evolutionary computer technology

Xue

2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

With the improvement of people’s living standards, people pay more and more attention to the indoor living environment. This research mainly discusses the research and realization of the innovative design of wall painting patterns based on the microprocessor system and the evolution of computer technology. Pattern design is an important field in art design. The understanding of pattern design in modern design is all patterns, graphics, and even symbols that can cause visual beauty and convey information. Its form can be flat or three-dimensional. Evolutionary Computation is a highly parallel, random and adaptive search algorithm developed based on natural selection and evolutionary mechanisms in the biological world. This article proposes a sub-graphics exchange method, the main idea is to achieve the overall aesthetic effect by exchanging the corresponding individual sub-graphics. The system can naturally store the pattern library. The wall painting works selected by the user and the simulation environment image are merged to generate a wall painting simulation effect diagram. In the process of wall painting pattern design, if you encounter a satisfactory pattern during evolution, you can save its parameters into the pattern database at any time. In the rendering simulation stage, if the user chooses to import wall photos by himself, the image format should be in jpg format, and the camera angle should be as close to the wall as possible, so that the wall painting pattern can be mapped vertically on the wall. The processor correctly realized the multi-core JPEG decoding function, and the system pattern processing efficiency reached 91%. The pattern design system designed in this study is highly innovative.

show abstract

Section: Evolution Wall Painting Patternmentioning

confidence: 99%

RETRACTED ARTICLE: Innovative design of wall painting pattern based on microprocessor system and evolutionary computer technology

Xue

2021

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…The performed analyses span several classification tasks and techniques: from style classification to artist identification, comprising also medium, school, and year classification [ 27 , 28 , 29 ]. These researches are useful to support cultural heritage studies and asset management, e.g., automatic cataloguing of unlabeled works in online and museum collections, but their results can be exploited for more complex applications, such as authentication, stylometry [ 30 ], and forgery detection [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis

et al. 2021

View full text Add to dashboard Cite

Iconography studies the visual content of artworks by considering the themes portrayed in them and their representation. Computer Vision has been used to identify iconographic subjects in paintings and Convolutional Neural Networks enabled the effective classification of characters in Christian art paintings. However, it still has to be demonstrated if the classification results obtained by CNNs rely on the same iconographic properties that human experts exploit when studying iconography and if the architecture of a classifier trained on whole artwork images can be exploited to support the much harder task of object detection. A suitable approach for exposing the process of classification by neural models relies on Class Activation Maps, which emphasize the areas of an image contributing the most to the classification. This work compares state-of-the-art algorithms (CAM, Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++) in terms of their capacity of identifying the iconographic attributes that determine the classification of characters in Christian art paintings. Quantitative and qualitative analyses show that Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++ have similar performances while CAM has lower efficacy. Smooth Grad-CAM++ isolates multiple disconnected image regions that identify small iconographic symbols well. Grad-CAM produces wider and more contiguous areas that cover large iconographic symbols better. The salient image areas computed by the CAM algorithms have been used to estimate object-level bounding boxes and a quantitative analysis shows that the boxes estimated with Grad-CAM reach 55% average IoU, 61% GT-known localization and 31% mAP. The obtained results are a step towards the computer-aided study of the variations of iconographic elements positioning and mutual relations in artworks and open the way to the automatic creation of bounding boxes for training detectors of iconographic symbols in Christian art images.

show abstract

“…The great advances in technologies related to image capture and the growth of other fields such as visual computing [ 7 , 8 ] have given the average user more power and capacity in image acquisition. From the user’s point of view, these new technologies create the expectation of more attractive images, but this also requires the knowledge and execution of basic aesthetic principles during capture and editing [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of the computational aesthetic evaluation is to simulate the visual system and human perception to make an aesthetic judgment about the images automatically [ 10 ]. In recent years, many researchers from different fields of knowledge, such as Artificial Intelligence, Psychology, Arts or Design, have focused on the identification of the characteristics most related to human aesthetic preferences, as well as on the modelling of computer systems to recreate human evaluations for classification and prediction tasks [ 7 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of an Aesthetic Assessment System for Commercial Tasks

Rodríguez-Fernández

Alvarez-Gonzalez

Santos

et al. 2022

Entropy

Self Cite

View full text Add to dashboard Cite

Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.

show abstract

Artificial Neural Networks and Deep Learning in the Visual Arts: a review

Cited by 72 publications

References 180 publications

RETRACTED ARTICLE: Innovative design of wall painting pattern based on microprocessor system and evolutionary computer technology

RETRACTED ARTICLE: Innovative design of wall painting pattern based on microprocessor system and evolutionary computer technology

Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis

Validation of an Aesthetic Assessment System for Commercial Tasks

Contact Info

Product

Resources

About