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Title: Personality-assisted multi-task learning for generic and personalized image aesthetics assessment
Authors: Li, Leida
Zhu, Hancheng
Zhao, Sicheng
Ding, Guiguang
Lin, Weisi
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Li, L., Zhu, H., Zhao, S., Ding, G. & Lin, W. (2020). Personality-assisted multi-task learning for generic and personalized image aesthetics assessment. IEEE Transactions On Image Processing, 29, 3898-3910.
Journal: IEEE Transactions On Image Processing
Abstract: Traditional image aesthetics assessment (IAA) approaches mainly predict the average aesthetic score of an image. However, people tend to have different tastes on image aesthetics, which is mainly determined by their subjective preferences. As an important subjective trait, personality is believed to be a key factor in modeling individual's subjective preference. In this paper, we present a personality-assisted multi-task deep learning framework for both generic and personalized image aesthetics assessment. The proposed framework comprises two stages. In the first stage, a multi-task learning network with shared weights is proposed to predict the aesthetics distribution of an image and Big-Five (BF) personality traits of people who like the image. The generic aesthetics score of the image can be generated based on the predicted aesthetics distribution. In order to capture the common representation of generic image aesthetics and people's personality traits, a Siamese network is trained using aesthetics data and personality data jointly. In the second stage, based on the predicted personality traits and generic aesthetics of an image, an inter-task fusion is introduced to generate individual's personalized aesthetic scores on the image. The performance of the proposed method is evaluated using two public image aesthetics databases. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts in both generic and personalized IAA tasks.
ISSN: 1057-7149
DOI: 10.1109/TIP.2020.2968285
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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