CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
Gourmet Photography Dataset for Aesthetic Assessment of Food Images
Kekai, Sheng1,2; Weiming, Dong1; Haibin, Huang3; Chongyang, Ma4; Bao-Gang, Hu1
Conference NameSIGGRAPH Asia 2018 Technical Briefs
Conference Date2018-12-4 2018-12-7
Conference PlaceTokyo, Japan

In this study, we present the Gourmet Photography Dataset (GPD), which is the first large-scale dataset for aesthetic assessment of food photographs. We collect 12,000 food images together with human-annotated labels (i.e., aesthetically positive or negative) to build this dataset. We evaluate the performance of several popular machine learning algorithms for aesthetic assessment of food images to verify the effectiveness and importance of our GPD dataset. Experimental results show that deep convolutional neural networks trained on GPD can achieve comparable performance with human experts in this task, even on unseen food photographs. Our experiments also provide insights to support further study and applications related to visual analysis of food images.

MOST Discipline Catalogue工学 ; 工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Document Type会议论文
Affiliation1.NLPR, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Megvii/Face++ Research
4.Snap Inc.
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Kekai, Sheng,Weiming, Dong,Haibin, Huang,et al. Gourmet Photography Dataset for Aesthetic Assessment of Food Images[C],2018.
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