Image aesthetic assessment using a saliency symbiosis network

Xuewei Li; Xueming Li; Gang Zhang; Xianlin Zhang

doi:10.1117/1.JEI.28.2.023008

8 March 2019 Image aesthetic assessment using a saliency symbiosis network

Xuewei Li, Xueming Li, Gang Zhang, Xianlin Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 023008 (March 2019). https://doi.org/10.1117/1.JEI.28.2.023008

Abstract

As a result of people taking more and more pictures in their lives, image assessment technology, which can automatically help people choose high quality pictures quickly, has become particularly important. Most algorithms use peak signal-to-noise ratio (PSNR) to assess image quality. However, images with high scores on PSNR are not as beautiful as individuals think. Image aesthetic assessment technology can come closer to human aesthetic standards. We report on a method named saliency symbiosis network for image aesthetic assessment. This is significant because we improved the conventional convolutional neural networks (CNN) method, which gets very close to the human visual mechanism after adding saliency features in CNN. Owing to considering limitations of CNN input size, we also proposed a pooling strategy to improve the ability of the model to accept arbitrary input sizes. Afterward, we propose an effective mean Huber loss function, which becomes less sensitive to outliers and can quickly train the model to being optimal. The experiment results proved that the proposed method, by using very small training data, performed the highest accuracy in image aesthetic assessment and classification.

Citation Download Citation

Xuewei Li, Xueming Li, Gang Zhang, and Xianlin Zhang "Image aesthetic assessment using a saliency symbiosis network," Journal of Electronic Imaging 28(2), 023008 (8 March 2019). https://doi.org/10.1117/1.JEI.28.2.023008

Received: 20 November 2018; Accepted: 19 February 2019; Published: 8 March 2019

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