RAPID: Rating Pictorial Aesthetics using Deep Learning

Xin Lu
The Pennsylvania State University

Zhe Lin, Hailin Jin, Jianchao Yang
Adobe Research

James Z. Wang
The Pennsylvania State University

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 recently- published 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 classier 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 signicantly outperforms the state of the art on the AVA dataset.

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Citation: Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang and James Z. Wang, ``RAPID: Rating Pictorial Aesthetics using Deep Learning,'' Proceedings of the ACM Multimedia Conference, pp. 457-466, Orlando, Florida, ACM, November 2014.

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Last Modified: August 27, 2014
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