scholarly journals Understanding Aesthetic Evaluation Using Deep Learning

2020 ◽  
pp. 118-133 ◽  
Author(s):  
Jon McCormack ◽  
Andy Lomas
Keyword(s):  
Deep Learning ◽  
Aesthetic Evaluation

scholarly journals Self-Adaptive Computational Aesthetic Evaluation of Chinese Ink Paintings Based on Deep Learning

2021 ◽  
Vol 33 (9) ◽  
pp. 1349-1360
Author(s):  
Jiajing Zhang ◽  
Jinhui Yu ◽  
Yongwei Miao ◽  
Ren Peng
Keyword(s):  
Deep Learning ◽  
Aesthetic Evaluation ◽  
Self Adaptive

scholarly journals Image Cropping with Composition and Saliency Aware Aesthetic Score Map

2020 ◽  
Vol 34 (07) ◽  
pp. 12104-12111
Author(s):  
Yi Tu ◽  
Li Niu ◽  
Weijie Zhao ◽  
Dawei Cheng ◽  
Liqing Zhang
Keyword(s):  
Deep Learning ◽  
Convolutional Network ◽  
Aesthetic Quality ◽  
Aesthetic Evaluation ◽  
Fully Convolutional Network ◽  
Intrinsic Mechanism ◽  
Real World Applications ◽  
Benchmark Datasets ◽  
Score Map ◽  
The Aesthetic

Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we propose an interpretable image cropping model to unveil the mystery. For each image, we use a fully convolutional network to produce an aesthetic score map, which is shared among all candidate crops during crop-level aesthetic evaluation. Then, we require the aesthetic score map to be both composition-aware and saliency-aware. In particular, the same region is assigned with different aesthetic scores based on its relative positions in different crops. Moreover, a visually salient region is supposed to have more sensitive aesthetic scores so that our network can learn to place salient objects at more proper positions. Such an aesthetic score map can be used to localize aesthetically important regions in an image, which sheds light on the composition rules learned by our model. We show the competitive performance of our model in the image cropping task on several benchmark datasets, and also demonstrate its generality in real-world applications.

A multi-scene deep learning model for image aesthetic evaluation

2016 ◽  
Vol 47 ◽  
pp. 511-518 ◽  
Author(s):  
Weining Wang ◽  
Mingquan Zhao ◽  
Li Wang ◽  
Jiexiong Huang ◽  
Chengjia Cai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Aesthetic Evaluation ◽  
Deep Learning Model

Using Eye-tracking and Deep Learning Approach to Promote Naturalness in Urban Landscape

2020 ◽  
Author(s):  
Yeshan Qiu ◽  
Yugang Chen ◽  
Shengquan Che
Keyword(s):  
Deep Learning ◽  
Eye Tracking ◽  
Bayesian Network ◽  
Evaluation System ◽  
Public Perception ◽  
Urban Landscape ◽  
Evaluation Model ◽  
Landscape Composition ◽  
Evaluation Framework ◽  
Aesthetic Evaluation

<p>Promoting greenness and naturalness has been the integral goal in nature-based solutions for urban environments. Design and building appreciated landscape for subjective public perception is a key factor in the success of promoting urban greenness and naturalness. The current measures of naturalness are siloed from public appreciation and acceptance of urban landscape designs. Our goal is to use state-of-art methods combining traditional design perception evaluation to embed naturalness with public landscape aesthetic perceptions evaluation system. A deep learning and eye-tracking based approach to understand public aesthetic perceptions of landscape street-view images is developed and applied to a case study of Shanghai. We use machine deep learning techniques to identify and assess landscape composition with landscape images and in-situ captured data to study the influence of naturalness of public perceptions of landscape based on a Bayesian network aesthetic evaluation model. The methodology extend the present landscape aesthetic evaluation framework and has the potential to be implemented to much wider applications. Our results indicate a co-conception of naturalness and public appreciation as a proof-of-concept of nature-based solutions.</p><p>Key words：Eye-tracking；Deep Learning；Naturalness；Public aesthetic perceptions；Bayesian network aesthetic evaluation</p>

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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