Rapid: Rating pictorial aesthetics using deep learning

Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Z. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

299 Scopus citations


Effective visual features are essential for computational aesthetic quality rating systems. Existing methods used machine learning and statistical modeling techniques on handcrafted features or generic image descriptors. A recentlypublished large-scale dataset, the AVA dataset, has further empowered machine learning based approaches. We present the RAPID (RAting PIctorial aesthetics using Deep learning) system, which adopts a novel deep neural network approach to enable automatic feature learning. The central idea is to incorporate heterogeneous inputs generated from the image, which include a global view and a local view, and to unify the feature learning and classifier training using a double-column deep convolutional neural network. In addition, we utilize the style attributes of images to help improve the aesthetic quality categorization accuracy. Experimental results show that our approach significantly outperforms the state of the art on the AVA dataset.

Original languageEnglish (US)
Title of host publicationMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450330633
StatePublished - Nov 3 2014
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: Nov 3 2014Nov 7 2014

Publication series

NameMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia


Other2014 ACM Conference on Multimedia, MM 2014
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software


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