Automatic orientation detection of abstract painting

Bai, Ruyi; Guo, Xiaoying

doi:10.1016/j.knosys.2021.107240

Cited by 14 publications

(12 citation statements)

References 8 publications

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“…Our general finding that deep learning methods can successfully encode visual information and detect variations among drawings is consistent with that of previous studies on abstract paintings. For example, [ 24 ] used a neural network to detect the correct orientation in such paintings, and [ 25 ] used deep learning to recognize art styles in paintings, notably, abstract art. Our results extend these reports by showing that deep learning can discover structured variations that have often been considered scribbles with unpredictable variations in the literature [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

Beltzung

Péle

Renoult

et al. 2022

Animals

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Drawings have been widely used as a window to the mind; as such, they can reveal some aspects of the cognitive and emotional worlds of other animals that can produce them. The study of non-human drawings, however, is limited by human perception, which can bias the methodology and interpretation of the results. Artificial intelligence can circumvent this issue by allowing automated, objective selection of features used to analyze drawings. In this study, we use artificial intelligence to investigate seasonal variations in drawings made by Molly, a female orangutan who produced more than 1299 drawings between 2006 and 2011 at the Tama Zoological Park in Japan. We train the VGG19 model to first classify the drawings according to the season in which they are produced. The results show that deep learning is able to identify subtle but significant seasonal variations in Molly’s drawings, with a classification accuracy of 41.6%. We use VGG19 to investigate the features that influence this seasonal variation. We analyze separate features, both simple and complex, related to color and patterning, and to drawing content and style. Content and style classification show maximum performance for moderately complex, highly complex, and holistic features, respectively. We also show that both color and patterning drive seasonal variation, with the latter being more important than the former. This study demonstrates how deep learning can be used to objectively analyze non-figurative drawings and calls for applications to non-primate species and scribbles made by human toddlers.

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Section: Discussionmentioning

confidence: 99%

Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

Beltzung

Péle

Renoult

et al. 2022

Animals

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“…e characteristic of this algorithm is to judge whether a user is interested in a certain content, and it does not need how high the weight of the interested option is, as long as it is higher than the average value [24].…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Supervision and Assistance Based on Mobile Information System in Art Video Teaching

Shi

2022

Computational Intelligence and Neuroscience

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To enrich students’ learning methods, improve their interest in learning, and enable students to fully understand and master the content of art video teaching, a supervision and assistance function based on mobile information system in art video teaching is proposed. Starting with the purpose of improving the auxiliary effect of art video teaching, this study deeply discusses the construction of personalized art video mobile information teaching system based on mobile information technologies such as self-media auxiliary technology and computer-aided technology. Therefore, this study takes android technology mobile information system as a breakthrough to design the video teaching system and introduces the design of mobile teaching information platform in detail. At the same time, according to the relevant requirements of art video teaching, this study designs the teaching system from the aspects of improving students’ learning interest, promoting students’ curriculum preference information, efficient real-time teacher-student interaction, and so on. According to the requirements of the six sections, the function of the teaching supervision and management module is improved. Through the test of result extraction and data analysis, the feasibility of the user preference extraction and analysis algorithm and the content similarity discrimination algorithm is finally verified. The results show that after more than 10 times of reading, the graphics of video teaching content can basically solve the problem of comparative similarity of the same type of teaching content. At the same time, after more than 30 times of reading, the teaching content can basically solve the problem of small probability error of user system.

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“…Furthermore, a neural network model is constructed to better express the rotation features of images, which can realize automatic directional detection and feature extraction of abstract paintings. By applying the extracted features to product design, the design speed is improved to a certain extent [3]. Wei proposed a painting image style feature extraction algorithm based on intelligent vision [4].…”

Section: Related Workmentioning

confidence: 99%

Design of Watercolor Cultural and Creative Products Based on Style Transfer Algorithm

Wang

Alamusi.²

2022

Mathematical Problems in Engineering

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Due to the long design cycle of watercolor cultural and creative products, a design method based on style transfer algorithm is proposed. Here, the design of Shanghai style watercolor painting cultural and creative products is taken as the research object, VGG-19 network is used as the style transfer model, and the loss function of style transfer algorithm is designed. The simulation results show that the proposed method can effectively extract and reconstruct the color, texture, brushstroke, and other styles of Shanghai style watercolor painting. The generated new images are added as design elements, which makes the cultural and creative products have both appearance and personalization. In addition, the newly generated cultural and creative products have also obtained high satisfaction from experts and users, which meet the aesthetic changes. It can be seen that proposed method provides a new idea for the design of watercolor cultural and creative products, which shortens the product design cycle, reduces the design cost, and has a certain practicality.

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Automatic orientation detection of abstract painting

Cited by 14 publications

References 8 publications

Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

Supervision and Assistance Based on Mobile Information System in Art Video Teaching

Design of Watercolor Cultural and Creative Products Based on Style Transfer Algorithm

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