Modeling aesthetic preferences: Color coordination and fuzzy sets

Turdybay

et al. 2022

PeerJ Computer Science

Self Cite

Nowadays, people get increasingly attached to social media to connect with other people, to study, and to work. The presented article uses Twitter posts to better understand public opinion regarding the vegan (plant-based) diet that has traditionally been portrayed negatively on social media. However, in recent years, studies on health benefits, COVID-19, and global warming have increased the awareness of plant-based diets. The study employs a dataset derived from a collection of vegan-related tweets and uses a sentiment analysis technique for identifying the emotions represented in them. The purpose of sentiment analysis is to determine whether a piece of text (tweet in our case) conveys a negative or positive viewpoint. We use the mutual information approach to perform feature selection in this study. We chose this method because it is suitable for mining the complicated features from vegan tweets and extracting users’ feelings and emotions. The results revealed that the vegan diet is becoming more popular and is currently framed more positively than in previous years. However, the emotions of fear were mostly strong throughout the period, which is in sharp contrast to other types of emotions. Our findings place new information in the public domain, which has significant implications. The article provides evidence that the vegan trend is growing and new insights into the key emotions associated with this growth from 2010 to 2022. By gaining a deeper understanding of the public perception of veganism, medical experts can create appropriate health programs and encourage more people to stick to a healthy vegan diet. These results can be used to devise appropriate government action plans to promote healthy veganism and reduce the associated emotion of fear.

Section: Methodsmentioning

confidence: 99%

Sentiment analysis of vegan related tweets using mutual information for feature selection

Turdybay

et al. 2022

PeerJ Computer Science

Self Cite

Front. Comput. Neurosci.

“…Researchers found that image aesthetic quality can be affected by numerous factors, including lighting ( Freeman, 2007 ), contrast ( Itten, 1975 ), color scheme ( Shamoi et al, 2020 ), and image composition ( London et al, 2011 ), etc. While judging the aesthetic quality of remote sensing images, viewers also have certain criteria or pay attention to certain features in mind.…”

Section: Methodsmentioning

confidence: 99%

An interpretable approach for automatic aesthetic assessment of remote sensing images

Tong

Zhang

Kong

et al. 2022

The increase of remote sensing images in recent decades has resulted in their use in non-scientific fields such as environmental protection, education, and art. In this situation, we need to focus on the aesthetic assessment of remote sensing, which has received little attention in research. While according to studies on human brain’s attention mechanism, certain areas of an image can trigger visual stimuli during aesthetic evaluation. Inspired by this, we used convolutional neural network (CNN), a deep learning model resembling the human neural system, for image aesthetic assessment. So we propose an interpretable approach for automatic aesthetic assessment of remote sensing images. Firstly, we created the Remote Sensing Aesthetics Dataset (RSAD). We collected remote sensing images from Google Earth, designed the four evaluation criteria of remote sensing image aesthetic quality—color harmony, light and shadow, prominent theme, and visual balance—and then labeled the samples based on expert photographers’ judgment on the four evaluation criteria. Secondly, we feed RSAD into the ResNet-18 architecture for training. Experimental results show that the proposed method can accurately identify visually pleasing remote sensing images. Finally, we provided a visual explanation of aesthetic assessment by adopting Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important image area that influenced model’s decision. Overall, this paper is the first to propose and realize automatic aesthetic assessment of remote sensing images, contributing to the non-scientific applications of remote sensing and demonstrating the interpretability of deep-learning based image aesthetic evaluation.

“…In their study, a total of 10,000 images with fashion looks were collected to form the fashion database. The result shows that users' preference for color schemes is influenced by preferences for the component basic colors and ratings of color harmony [23]. In the study of N.…”

Section: Product Aesthetic Modelingmentioning

confidence: 95%

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

Xing

et al. 2020

IEEE Access

A visual aesthetic is a crucial determinant of product design evaluation. Through the analysis of image features, not only can we evaluate the aesthetic level, but also we can reveal the whole quality of the design proposal. We assume that it could be a potential pattern to predict the ultimate success of the proposal in product design that a visual aesthetic can be a cue for award classification modeling. Consequently, we conduct investigation on a dataset of over 10,003 design submissions in a design competition held once a year from 2008 to 2018 in order to manifest the assumption. Due to the remarkable performance of deep convolutional neural networks (DCNNs), we compare seven deep learning methods to explore an optimal model for design award prediction based on product image analysis. The result of the experiments indicates that the proposed method achieves comparative accuracy in design award classification result predication, with the optimal classification accuracy of 70.79% using the SEFL-ResNet (Squeeze and Excitation-Focal Loss-ResNet) method.