publication . Part of book or chapter of book . Conference object . 2019

image aesthetics assessment using fully convolutional neural networks

Apostolidis, Konstantinos; Mezaris, Vasileios;
Open Access
  • Published: 10 Jan 2019
  • Publisher: Springer International Publishing
Abstract
This paper presents a new method for assessing the aesthetic quality of images. Based on the findings of previous works on this topic, we propose a method that addresses the shortcomings of existing ones, by: (a) Making possible to feed higher-resolution images in the network, by introducing a fully convolutional neural network as the classifier. (b) Maintaining the original aspect ratio of images in the input of the network, to avoid distortions caused by re-scaling. And (c) combining local and global features from the image for making the assessment of its aesthetic quality. The proposed method is shown to achieve state of the art results on a standard large-s...
Subjects
free text keywords: Image Aesthetics, Fully Convolutional Neural Networks, Artificial intelligence, business.industry, business, Pattern recognition, Deep learning, Convolutional neural network, Computer science, Classifier (linguistics)
Funded by
EC| EMMA
Project
EMMA
Enriching Market solutions for content Management and publishing with state of the art multimedia Analysis techniques
  • Funder: European Commission (EC)
  • Project Code: 732665
  • Funding stream: H2020 | IA
,
EC| InVID
Project
InVID
In Video Veritas – Verification of Social Media Video Content for the News Industry
  • Funder: European Commission (EC)
  • Project Code: 687786
  • Funding stream: H2020 | IA
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publication . Part of book or chapter of book . Conference object . 2019

image aesthetics assessment using fully convolutional neural networks

Apostolidis, Konstantinos; Mezaris, Vasileios;