[Invited papers] A Survey on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia

Chu, Wei-Ta; Motomura, Hideto; Tsumura, Norimichi; Yamasaki, Toshihiko

doi:10.3169/mta.7.60

Cited by 3 publications

(3 citation statements)

References 55 publications

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“…Video art [1,2] is closely related to the development of contemporary technology and is widely used in various fields of social life. Video art has become one of the main media forms of contemporary art.…”

Section: Introductionmentioning

confidence: 99%

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Gong

2022

Security and Communication Networks

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Video artworks are closely linked with the development of contemporary technology. Therefore, it is widely used in various fields of social life. Video art has become one of the main media forms of contemporary art. In the practice of art teaching, how to combine the existing content of traditional art teaching with video technology and how to understand the inner connection between traditional aesthetics and technological aesthetics have become issues that workers in the new era must think about and pay attention to. As a typical case of influencing works of art, movie animation is loved by the majority of young people. In order to quantify the application effect of machine learning in video art and film animation text mining, this paper conducts prediction research and analysis on several main aspects of color features involved in film animation. By introducing three typical machine learning methods, this paper analyzes the distribution law of the color features of film animation from the perspective of machine learning and its influence on artistic texts. Specifically, the paper uses machine learning methods as a carrier to predict the performance of multiple main modules of color features in movie animation. The prediction results show that the square of the correlation coefficient corresponding to the extreme learning machine is the largest, and the root mean square error, the mean absolute percentage error, and the median absolute error are the smallest, which shows that the extreme learning machine has the best prediction effect. Therefore, it corresponds to the best prediction. In addition, the comparison between the predicted data and the measured data shows that the relationship between the two is approximately a linear function of y = x. At the same time, the fitting calculation shows that the predicted data corresponding to the two main modules of the main color and the color structure in the color feature exhibit a good functional relationship of polynomial functions.

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Section: Introductionmentioning

confidence: 99%

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Gong

2022

Security and Communication Networks

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“…For many years, technological progress has wrought profound changes in the creative industry, as in the fields of music (e.g., [18,19]), parametric design (e.g., [41]), generative fashion design (e.g., [34]), and paintings (e.g., [23]). This has given rise to the emergence of new research fields: computational aesthetics [31], attractiveness computing (see Chu [14] for a literature review), Creative Adversarial Networks (CAN) [23] and computational creativity [16]. Computational creativity is a generic field of artificial intelligence (AI) that studies both artificial and human creativity [56] and has grown considerably.…”

Section: Introductionmentioning

confidence: 99%

“…Artists have appropriated these models have been in recent years to the point that they have become a new art market [20]. Thus, a first art painting generated by a GAN was sold for $432,500 by Obvious [14] 2 . With the increase in quality and ubiquity of creative systems such as GANs, studying these creativity support systems seems to be necessary [17,29].…”

Section: Introductionmentioning

confidence: 99%

AI-generated vs. Human Artworks. A Perception Bias Towards Artificial Intelligence?

Ragot

Martin

Cojean

2020

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

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Via generative adversarial networks (GANs), artificial intelligence (AI) has influenced many areas, especially the artistic field, as symbol of a human task. In human-computer interaction (HCI) studies, perception biases against AI, machines, or computers are generally cited. However, experimental evidence is still lacking. This paper presents a wide-scale experiment in which 565 participants are asked to evaluate paintings (which were created by humans or AI) on four dimensions: liking, perceived beauty, novelty, and meaning. A priming effect is evaluated using two between-subject conditions: artworks presented as created by an AI, and artworks presented as created by a human artist. Finally, the paintings perceived as being drawn by human are evaluated significantly more highly than those perceived as being made by AI. Thus, using such a methodology and sample in an unprecedented way, the results show a negative bias of perception towards AI and a preference bias towards human systems.

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A comprehensive survey on object detection in Visual Art: taxonomy and challenge

Bengamra

Mzoughi

Bigand

et al. 2023

Multimed Tools Appl

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[Invited papers] A Survey on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia

Cited by 3 publications

References 55 publications

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

Text Mining of Movie Animation User Comments and Video Artwork Recommendation Based on Machine Learning

AI-generated vs. Human Artworks. A Perception Bias Towards Artificial Intelligence?

A comprehensive survey on object detection in Visual Art: taxonomy and challenge

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