Predicting Image Aesthetics with Deep Learning

  • Simone Bianco
  • Luigi Celona
  • Paolo Napoletano
  • Raimondo Schettini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)


In this paper we investigate the use of a deep Convolutional Neural Network (CNN) to predict image aesthetics. To this end we fine-tune a canonical CNN architecture, originally trained to classify objects and scenes, by casting the image aesthetic prediction as a regression problem. We also investigate whether image aesthetic is a global or local attribute, and the role played by bottom-up and top-down salient regions to the prediction of the global image aesthetic. Experimental results on the canonical Aesthetic Visual Analysis (AVA) dataset show the robustness of the solution proposed, which outperforms the best solution in the state of the art by almost 17 % in terms of Mean Residual Sum of Squares Error (MRSSE).


Support Vector Regression Convolutional Neural Network Salient Region Aesthetic Quality Fisher Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Simone Bianco
    • 1
  • Luigi Celona
    • 1
  • Paolo Napoletano
    • 1
  • Raimondo Schettini
    • 1
  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanItaly

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