Subjective Ratings of <i>Beauty</i> and <i>Aesthetics</i>: Correlations With Statistical Image Properties in Western Oil Paintings

Hayn‐Leichsenring, Gregor U.; Redies, Christoph

doi:10.1177/2041669517715474

Cited by 54 publications

(57 citation statements)

References 46 publications

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“…On the other hand, Forsythe et al [9] found a stronger relationship between a combination of their image complexity statistics (GIF image size and fractal dimension) and beauty ratings (42% shared variance). However, a recent paper [10] found no more than 5.6% shared variance between their artwork statistics and ratings. When analyses were performed separately for artworks containing different subjects, shared variance increased, and was highest for flowers/vegetation (13%) and for buildings (25.3%).…”

Section: Introductionmentioning

confidence: 94%

“…When analyses were performed separately for artworks containing different subjects, shared variance increased, and was highest for flowers/vegetation (13%) and for buildings (25.3%). Other recent research has focused on identifying clusters of participants who share common statistical preferences [10][11][12] The variable success of attempts to identify simple statistical measures of universal beauty is not surprising given that artworks vary along multiple visual dimensions and in terms of their semantic content: as well as containing luminance and colour variation at a range of spatial scales, artworks also often contain meaningful subject matter, such as a landscape, a human face or other biological forms. Given this complexity, beauty ratings may depend on a combination different image statistics rather than a single statistic, and the relevant combination of statistics may vary across subject matter.…”

Section: Introductionmentioning

confidence: 99%

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Aesthetic Image Statistics Vary with Artistic Genre

Mather

2020

Vision

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Research to date has not found strong evidence for a universal link between any single low-level image statistic, such as fractal dimension or Fourier spectral slope, and aesthetic ratings of images in general. This study assessed whether different image statistics are important for artistic images containing different subjects and used partial least squares regression (PLSR) to identify the statistics that correlated most reliably with ratings. Fourier spectral slope, fractal dimension and Shannon entropy were estimated separately for paintings containing landscapes, people, still life, portraits, nudes, animals, buildings and abstracts. Separate analyses were performed on the luminance and colour information in the images. PLSR fits showed shared variance of up to 75% between image statistics and aesthetic ratings. The most important statistics and image planes varied across genres. Variation in statistics may reflect characteristic properties of the different neural sub-systems that process different types of image.

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Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Aesthetic Image Statistics Vary with Artistic Genre

Mather

2020

Vision

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“…They allow the creation of models that can analyze any picture and predict their aesthetic value, without the need for any annotated data about its contents; and without making use of hand-crafted features. Some examples of the use of CNNs for image aesthetics prediction and related topics can be found in [2,20,33,6,11,4,9,35,10,17]. Some of those papers make use of information about the contents of the pictures to improve the predictions of the models.…”

Section: Related Work 21 Computational Aesthetic Assessment In Photomentioning

confidence: 99%

“…The features in which the piece of art can be described with will, therefore, explain only a small part of the preferences of human perceivers over aesthetics in art. Nevertheless, it has been suggested that there is some shared perception of beauty and aesthetics in art [11], which suggests that there are features that computational models can learn which account for this shared perception of beauty.…”

Section: Introductionmentioning

confidence: 99%

Personalised Aesthetics with Residual Adapters

Rodríguez-Pardo

Bilen

2019

Pattern Recognition and Image Analysis

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The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, userspecific preferences over aesthetics in photography, while surpassing the stateof-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.The problem of taking into account subjective preferences on image aesthetics prediction is referred to as personalized image aesthetics [27]. Most recent approaches to image aesthetics evaluation have used different deep-learning models, which require a significant amount of annotated data for their training and evaluation. In real-world situations, it is unrealistic to assume that we will have thousands of annotated examples of rated images for any given user. This puts limits on the use of deep learning models for personalized image aesthetics prediction.In order to train a machine learning model capable of taking into account individual preferences over aesthetics in photography, an annotated dataset with the identities of the raters of each picture is needed. One example of this kind of dataset is the FLICKER-AES dataset, presented by Ren et al. [27], which contains over 40000 images rated by more than 200 different human raters. Their study provides, along with this dataset (and another, smaller, dataset), a residual-based learning model capable of taking into account user-specific preferences over aesthetics in photography.We build on their work, and propose an end-to-end convolutional neural network model capable of modelling user-specific preferences with different levels of abstraction, while keeping a reduced number of user-specific parameters within the model. Our method models user-specific preferences by using residual adapters, which were presented in [26,25] and have shown success in multi-domain learning. The main difference between our model and Ren et al.'s is that they model user-specific preferences by first training a generic aesthetics network, which predicts a mean aesthetic score, and computes a user-specific offset by training a Support Vector Regressor using the predicted content and some manually-defined attributes of the picture as its input; whereas our model embeds the user-specific parameters in different layers of the neural network, therefore allowing the model to find user-specific features with different levels of abstraction, and which do not necessarily depend on the contents and a fixed set of attributes of the pictures.Our main contributions are as follows: First, we propo...

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“…The evaluation of the quality of goods is based on human feelings in many areas, such as works of art [1,2], consumer electronics [3,4], cars [5,6], and other products [7,8]. Subjective evaluations are as important as objective metrics for engineering products because consumers evaluate the value of products based on their feelings.…”

Section: Introductionmentioning

confidence: 99%

Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction

Kim

Jeong

Bae

et al. 2019

Sensors

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The subjective evaluation of vehicle ride comfort is costly and time-consuming but is crucial for vehicle development. To reduce the cost and time, the objectification of subjective evaluation has been widely studied, and most of the approaches use a regression model between objective metrics and subjective ratings. However, the accuracy of these approaches is highly dependent on the selection of the objective metrics. In most of the methods, it is not clear that the selected metrics are sufficiently significant or whether all significant metrics are included in the selection. This paper presents a method to build a correlation model between measurements and subjective evaluations without using predefined features or objective metrics. A numerical representation of ride comfort was extracted from raw signals based on the idea of the artistic style transfer method. The correlation model was designed based on the extracted numerical representation and subjective ratings. The model has a much better accuracy than any other correlation models in the literature. This better accuracy is contributed to not only by using a neural network, but also by the extraction of the numerical representation of ride comfort using a pre-trained neural network.

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Subjective Ratings of Beauty and Aesthetics: Correlations With Statistical Image Properties in Western Oil Paintings

Cited by 54 publications

References 46 publications

Aesthetic Image Statistics Vary with Artistic Genre

Aesthetic Image Statistics Vary with Artistic Genre

Personalised Aesthetics with Residual Adapters

Design of a Human Evaluator Model for the Ride Comfort of Vehicle on a Speed Bump Using a Neural Artistic Style Extraction

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