Computational Aesthetics Research Articles

Analysis of the relationship between visual complexity and SEO rank of library websites affiliated with top universities

Purpose Computational aesthetics involves the application of computer-based methods to analyse and understand various phenomena. Given the significance of university library websites in the advancement of science and knowledge, it is essential to consider their aesthetic qualities in conjunction with their web visibility, particularly regarding search engine optimisation (SEO). This study aims to investigate the relationship between visual complexity as an important aspect of the aesthetics of a website and its SEO rankings of top university library websites. Design/methodology/approach This study employed an analytical survey research design, analysing 35 library websites affiliated with top universities as ranked by Times Higher Education in 2023. Visual complexity was assessed using Python Athec programming, a tool specifically developed for computational aesthetic analysis in social science research. Additionally, the AIOSEO online smart tool was utilised to extract SEO scores. Data analysis was conducted using SPSS and Excel. Findings The study found no significant correlation between visual complexity and SEO rankings, with a significance level of 0.125 indicated by linear regression and correlation analyses. This suggests that top university library websites may not require extensive SEO optimisation due to their established credibility and branding. Notably, even those with lower SEO rankings continue to attract international users. Originality/value This research is distinguished by its innovative use of Python programming to measure user experience in the context of aesthetics. It offers new insights into the field of computational aesthetics for the managers of libraries.

The Application of Ceramic Materials in Tourism Product Design for Craft Arts Majors

In the realm of tourism product design, the integration of traditional arts and crafts with modern technological advancements presents a compelling opportunity for innovation. This study focuses on the utilization of ceramic materials as a cultural anchor for tourism product development. By proposing an optimized model based on improved Convolutional Neural Networks (CNN), the research explores the enhanced analysis and artistic expression capabilities of ceramic tourism products. The model's effectiveness is demonstrated through achieving over 95% accuracy in identifying and localizing ceramic product appearance features. Emphasizing the intersection of three-dimensional parametric technology and computer aesthetics, the study highlights advancements in digital modeling that elevate the design efficiency and cultural resonance of ceramic art within the tourism industry. This work contributes to advancing arts and crafts education and underscores the transformative impact of digital technologies on traditional craftsmanship and cultural heritage preservation.

Evaluating the Temporal Effect of Image Features on Dynamic Aesthetic Experience of Generative art

The emergence of dynamic digital arts, such as dynamic generative art, has reshaped how aesthetic experiences can be studied, emphasizing their inherently dynamic and evolving nature. Within the framework of computational aesthetics, which seeks to model and quantify human perceptions of beauty, this study extends the focus from static to dynamic stimuli. We investigate the temporal relationship between evolving image features and aesthetic judgments, exploring how dynamic visual properties influence the progression of aesthetic judgments over time. By introducing temporal offsets in the analysis and employing linear and nonlinear statistical models, we examine continuous aesthetic ratings alongside an extensive set of image features. Our findings offer a novel perspective on the enduring impact of image features on aesthetic experience.

Machines That Create

In this article, M. Beatrice Fazi takes up Media Theory’s invitation to engage with Alan Díaz Alva’s analysis of her philosophical work on contingency in computation. The central argument of Fazi’s Contingent Computation: Abstraction, Experience, and Indeterminacy in Computational Aesthetics is that computation can be productive of ontological novelty. This piece revisits that argument in the light of the technological developments that have occurred since 2018, when the book was published. Focusing on generative artificial intelligence (generative AI), the article considers how the concepts of generativity, aesthetics and contingency are tackled in the book and in present-day debates about generative AI systems.

Investigating the Relationship between Balanced Composition and Aesthetic Judgment through Computational Aesthetics and Neuroaesthetic Approaches

Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants’ data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300–500 ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600–1000 ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC = 0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC = 0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multi-modal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences.

A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks

This paper introduces a computational aesthetics framework utilizing computer vision (CV) and artificial neural networks (ANN) to predict the aesthetic preferences of groups of people for architecture. It relies on part-to-whole theories from aesthetics and cognitive psychology. A survey of a group of people on preferences of images is held to record an average hedonic response (AHR). CV algorithms MSER and SAM recognize parts in images. Birkhoff’s aesthetic measure formula is adapted by employing the number of parts and their connections. These quantities are used as input layers of an ANN, and the AHR is the target output. The ANN evaluates images to output a predicted hedonic response (PHR), which is tested as a criterion in parametric design space navigation and in mapping the latent space of GANs. We conclude that such a framework is a heuristic method for better understanding the design and latent spaces and exploring designs.

Exploring the Influence of Object, Subject, and Context on Aesthetic Evaluation through Computational Aesthetics and Neuroaesthetics

Background: In recent years, computational aesthetics and neuroaesthetics have provided novel insights into understanding beauty. Building upon the findings of traditional aesthetics, this study aims to combine these two research methods to explore an interdisciplinary approach to studying aesthetics. Method: Abstract artworks were used as experimental materials. Based on traditional aesthetics and in combination, features of composition, tone, and texture were selected. Computational aesthetic methods were then employed to correspond these features to physical quantities: blank space, gray histogram, Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filters. An electroencephalogram (EEG) experiment was carried out, in which participants conducted aesthetic evaluations of the experimental materials in different contexts (genuine, fake), and their EEG data were recorded to analyze the impact of various feature classes in the aesthetic evaluation process. Finally, a Support Vector Machines (SVMs) was utilized to model the feature data, Event-Related Potentials (ERPs), context data, and subjective aesthetic evaluation data. Result: Behavioral data revealed higher aesthetic ratings in the genuine context. ERP data indicated that genuine contexts elicited more negative deflections in the prefrontal lobes between 200 and 1000 ms. Class II compositions demonstrated more positive deflections in the parietal lobes at 50–120 ms, while Class I tones evoked more positive amplitudes in the occipital lobes at 200–300 ms. Gabor features showed significant variations in the parieto-occipital area at an early stage. Class II LBP elicited a prefrontal negative wave with a larger amplitude. The results of the SVM models indicated that the model incorporating aesthetic subject and context data (ACC = 0.76866) outperforms the model using only parameters of the aesthetic object (ACC = 0.68657). Conclusion: A positive context tends to provide participants with a more positive aesthetic experience, but abstract artworks may not respond to this positivity. During aesthetic evaluation, the ERP data activated by different features show a trend from global to local. The SVM model based on multimodal data fusion effectively predicts aesthetics, further demonstrating the feasibility of the combined research approach of computational aesthetics and neuroaesthetics.

Critical Climate Machine: A Visual and Musical Exploration of Climate Misinformation through Machine Learning

Critical Climate Machine is a cutting-edge media art installation that critically exposes and quantifies mechanisms of climate change misinformation. Utilizing computational aesthetics across data, imagery, and sound, this work processes real-time data from X (Twitter) through a natural language processing learning model derived from cognitive sciences. It not only renders the statistical aspects of this data visually but also manifests its thermal effects. A unique audio dimension is introduced through dialogues between climate skeptics and climate advocates, processed by the generative machine learning (ML) algorithm Dicy2. These elements collectively shape the installation, each unveiling its distinctive algorithmic aesthetics and technical underpinnings. This paper concentrates on the dual application of ML algorithms: one for dissecting extensive online misinformation streams, and the other for creating climate-related dialogues. This dual approach opens a discussion on the mediation of climate, at the convergence of computational and physical realms. Our aim is to critically examine the role of ML technologies in crafting aesthetic experiences that resonate within scientific discourse and public debate on climate issues.

Automatic Design Line Detection in Urban Building Images through DCT

The conversion of urban building images into design lines is a valuable technique used in architecture. It aids in theanalysis of design styles and building components. Recent studies have shown a significant interest in usingcomputational methods to examine the design lines in architectural images, such as plans, facades, and streetscapes.This research presents an automated approach that utilizes kernel filters to extract edges and reduce noise fromvarious architectural images. Additionally, it introduces a new filter that separates lines based on their directionsutilizing the Discrete Cosine Transform (DCT). The proposed technique generates distinct images that display thevertical, horizontal, and curved lines extracted from the original images. The results demonstrate that the proposedtechnique is efficient not only with architectural drawings but also with photographs of existing buildings. Thistechnique opens the door for further experimentation in artificial intelligence, computational aesthetics, imagerectification and calibration, and 3D building reconstruction.

Computational assessment of the aesthetic quality of map colors

AbstractCrafting beautiful map colors is challenging for not only experts but also novices, suggesting the need for an aesthetic quality assessment of map colors for the effective design of maps and visualizations. To fill this gap, we present a computational method to assess the aesthetic quality of map colors. First, we couple the idea of computational aesthetics with map aesthetic principles and identify four and two basic types of aesthetic features in terms of order and complexity, respectively. Then, we collect 2000 map samples and derive 149 aesthetic metrics by instantiating the above aesthetic features while considering the spatial weights and figure–ground organization of the map samples. We also recruit participants (N = 438) to rate the aesthetic quality of the map colors. Finally, we train an aesthetic predictor by fitting those aesthetic metrics with user ratings. The experimental results show that the proposed method can assess aesthetic quality of map colors with high accuracy (R2 = 0.73 on the training set and R2 = 0.65 on the validation set). We also explore the dominant aesthetic metrics that positively and negatively influence aesthetic appreciation, as well as those metrics that have no significant influence. This work offers a portable and flexible aesthetic quality assessment approach for map colors and can be further improved by considering complex symbols, spatial structures, and color–semantic and color–emotion associations.

An interpretable metric of visual aesthetics for GUI design

ABSTRACT Computation-based aesthetics metrics have been developed to help designers predict visual aesthetics scores for GUI design. However, designers find these evaluative scores difficult to understand. This paper proposed an interpretable aesthetics metric for GUI design that integrates visual aesthetics (visual similarity and spatial proximity) and GUI structure (semantic similarity and white space) to model visual grouping distribution. Two experiments were conducted to validate the metric’s ability to predict aesthetics and interpret outputs. Experiment 1 showed that our metric had a stronger correlation with users’ impressions of GUI visual aesthetics than past metrics. Experiment 2 suggested that our metric was easier to interpret and appeared more useful to Visual/Graphic/GUI designers than a conventional score-based alternative, by visualising the metric outputs as an experimental tool. Furthermore, this paper provided five potential insights to further advance computational aesthetics research.

Research on Product Model Generation and Visual Presentation Incorporating Computational Aesthetics

Abstract In the era of aesthetic economy, users pay more and more attention to the aesthetic quality and emotional experience of products. The study discusses the intelligent design method of product model integrating computational aesthetics, combines the advantages of variational self-encoder and generative adversarial network, constructs a generative model based on VAE-GAN, and quantitatively analyzes the product images generated by the VAE-GAN network to explore the effect of the products it generates. Then, take the car form generated by the model as an example, establish the visual presentation quantitative index system of the product model form layout, and evaluate the visual presentation effect of the generated car model after applying gray correlation analysis to assign the indexes. The VAE-GAN model has a better generative effect, and its SSIM value is greater than 0.8 in most of the training phases. Among them, the product model generated after 2000 steps of training is the most excellent, with the highest evaluation score. The product model is the most outstanding, with the highest evaluation score of 1.92. The overall visual presentation effect of the product forms generated by the model is good, and the comprehensive evaluation scores are above 0.6. The VAE-GAN model can be used for product generation, and the visual presentation evaluation method that integrates computational aesthetics can realize the aesthetic evaluation of product forms, which can help designers make design creations and program decisions.

Identifying influences between artists based on artwork faces and geographic proximity

The investigation of influences in artists’ works has been a subject of interest for art historians for many years. Therefore, computational methods can provide a new perspective for identifying these influences’ relationships. Indeed, several studies in computer science have proposed techniques to analyze similarities between paintings using various features. Faces are a crucial aspect of perception in art and have also been the focus of several studies in computational aesthetics. In our previous work, we proposed a method for analyzing artworks and evaluating the influence of artists. The present study improves upon the previous research by extending the analysis of influences considering second-degree influences between artists and the impact of geographic proximity, obtaining better results in terms of Recall than the previous work. In addition, we evaluated the capability of our method to detect work-to-work relationships between each pair of artworks by the artists, and we found plausible and interesting results, even though they have not yet been proven in the literature. By conducting further analysis of data extracted from the faces of works of art, the goal is to enhance the previous findings in the literature and foster further discussion and collaboration between the fields of art and computer science. The objective is not to provide a definitive answer to the question of influences but to stimulate further research in this area, pointing out new possibilities of influence and explanations about these influences.

Rational Beauty in Computational Aesthetics

Rational Beauty in Computational Aesthetics

Salient-Centeredness and Saliency Size in Computational Aesthetics

We investigate the optimal aesthetic location and size of a single dominant salient region in a photographic image. Existing algorithms for photographic composition do not take full account of the spatial positioning or sizes of these salient regions. We present a set of experiments to assess aesthetic preferences, inspired by theories of centeredness, principal lines, and Rule-of-Thirds. Our experimental results show a clear preference for the salient region to be centered in the image and that there is a preferred size of non-salient border around this salient region. We thus propose a novel image cropping mechanism for images containing a single salient region to achieve the best aesthetic balance. Our results show that the Rule-of-Thirds guideline is not generally valid but also allow us to hypothesize in which situations it is useful and in which it is inappropriate.

Towards a New Computational Aesthetics of Creative Software

This paper proposes a deep analysis of the latest research of digital humanities scholar Beatrice Fazi, and especially her critique of computational automation, to understand the roles of digital creative technologies, and more specifically of creative software. After a close analysis of Fazi’s main contribution to a new understanding of computational aesthetics, we will briefly outline the potential implications of her work to understand the contemporary evolution of creative software, and especially the implementation of machine learning algorithms in these kinds of software. This will lead us to contextualise the contemporary anxieties regarding how machines could replace humans in the act of artistic creation.

Considering three elements of aesthetics: Multi-task self-supervised feature learning for image style classification

Image style classification is the basis of computational aesthetics, and its role has become increasingly important with the rise of computational aesthetics. Most of the current image style classification methods use supervised learning for model training. These methods require a large number of expensive aesthetic style labels. Unlike existing methods, self-supervised learning can perform feature learning on many current unlabeled style images, thereby alleviating the constraint that current supervised learning methods require a large amount of labeled data. However, the self-supervised learning method also poses the problem that it is difficult to fully characterize the highly subjective aesthetic style characteristics. Therefore, this study proposes a multi-task self-supervised style feature learning algorithm considering the three elements of aesthetics. The three elements of aesthetics include compositional rules, luminance and color. The algorithm designs multiple self-supervised learning tasks from multiple perspectives and proposes a joint learning method for multiple self-supervised learning tasks, so that the model can learn the style features of images more comprehensively than ordinary self-supervised learning methods. The experimental results on three large image style datasets also show that the method proposed in this paper can effectively learn the style features of images and outperform most style feature learning algorithms based on supervised learning in terms of classification results.

인간화 미학에 기반한 컴퓨팅 미학에 관한 연구

In recent years, computational aesthetics, which connects science and art, is becoming a new interdisciplinary field that brings technology into art creating. With advanced CAD technology and artificial intelligence, computational aesthetics has been widely used in many design fields such as architecture and industry design. Famous design companies such as Foster+Partners, Zaha Hadid and BIG have already adopted computational and parametric design method as a competitive way to enhance creativity. But there are still some argues that this new aesthetics is superficial or even cold. In terms of generative art, as an advanced digital art, reveals a new aesthetic power, which is impressive. In this context, the purpose of the study aims at comparing computational aesthetics with aesthetics to reveal their differentiation and correlation that benefit for designer to fresh the cognition of this new approach vice versa. As methods of this research, firstly previous researches and literature are collected and comparatively analyzed on the concept and relationships of aesthetics and computational aesthetics, as well as the assessment method. Secondly, through case studies, under the characters of these two aesthetic approaches in design field, three representative cases are selected to be deeply investigated by collecting and analyzing relevant articles, books and online materials to explain the respective features. Then, after comparison and further analysis, explaining how computational aesthetics as a rational aesthetic expression can augment the limit of human aesthetic experience and the design thinking process. The final results of this paper point that aesthetics dominated by emotional mind belongs to a top-down thinking process, while computational aesthetics because of its algorithmic generated basis, which dominated by logic mind, it belongs to a bottom-up thinking process. It is expected that the findings based on this research can provide certain guidance for designers to make good use of them.

Computational Analysis Problem of Aesthetic Content in Fine-Art Paintings

The article discusses the possibilities of the formal analysis of the fine-art painting composition on the basis of the classical definitions of beauty and computational aesthetics’ approaches of the second half of the 20th century he authors define the problem and consider solutions for the formalization of aesthetic perception in the context of aesthetic text, i.e., as part of the fine arts composition – a formal sequence of signs simply ordered in accordance with the syntactic rules’ system. The methodology of the research is defined by the general semiotics, distinguishing semantics, syntax, and pragmatics of a sign, by the aesthetic analysis’ methods, ranging according to the author’s message aesthetics, receptive aesthetics, and text aesthetics, as well as by the computational analysis methods connected with neural network means of defining the images’ symmetry. The article reveals preconditions for the emergence and also the current state of computational aesthetics as an interdisciplinary branch of knowledge. Analyzing the problem from the perspective of philosophy, aesthetics, semiotics, and technology, the authors draw attention to the need to improve the computational aesthetics methods. Firstly, the existing methods do not always enable to describe the fine-art object adequately. Secondly, there exists the so-called reduction of aesthetic assertion transforming it into the assertion concerning the object’s external characteristics. As a result, the authors assume that the increasing complexity of the current mathematical models and the experts’ subjective assessment support will allow to reach a compromise solution that enables the development of computational aesthetics as a branch of knowledge. Enhancement and development of the mathematical models, taking into account the rules of semiotics and subjectivism of the human perception, is the relevant objective of computational analysis of the aesthetic fine-arts text. The results of the present research supports the classic statement regarding the underivability of semantic and pragmatic propositions from syntax. The research concludes that relevant objectives are to find a correlation between, one the one hand, the axes and points of symmetry, deriving from the neural simulation, and, on the other hand, aesthetic effect, emerging from the perception of fine-art paintings.

A Multitask Convolutional Neural Network for Artwork Appreciation

The computational aesthetics of pictorial art is an important part of human artistic creation, and the computational aesthetics of pictorial art images is a computationally computable human aesthetic process using machines, which has important applications and scientific significance in the automated analysis of large-scale paintings and the computational modeling of perception by machines. To this end, this paper proposes a multitask convolutional neural network model for emotion and rating of artworks. (1) An artwork appreciation dataset consisting of fifty Chinese paintings and fifty Western oil paintings was created, and twenty subjects were recruited to score the art appreciation of one hundred artworks in the dataset, covering both painting aesthetic evaluation and painting emotion evaluation. (2) Based on the artwork art appreciation dataset, an AlexNet-based convolutional neural network model is proposed to utilize the powerful feature extraction and classification capabilities of neural networks to complete artwork art appreciation, and an oversampling method and multitask learning method are used to improve the overall recognition accuracy. (3) Compared with the combination of traditional manual features + machine learning algorithms, the end-to-end multitask convolutional neural network proposed in this paper has the highest accuracy rate of 74.57%/71.43%/74.12%.