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Deep Aesthetic Quality Assessment With Semantic Information
Yueying Kao1,2,3; Ran He1,3,4; Kaiqi Huang1,2,3,4
AbstractHuman beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the aesthetic task. In particular, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging Aesthetic Visual Analysis dataset and Photo. net dataset validate the importance of semantic recognition in aesthetic quality assessment, and demonstrate that multi-task deep models can discover an effective aesthetic representation to achieve the state-of-the-art results.
KeywordVisual Aesthetic Quality Assessment Semantic Information Multi-task Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Natural Science Foundation of China(61322209 ; International Partnership Program of Chinese Academy of Science(173211KYSB20160008) ; 61673375 ; 61403387)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000395837700033
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yueying Kao,Ran He,Kaiqi Huang. Deep Aesthetic Quality Assessment With Semantic Information[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(3):1482-1495.
APA Yueying Kao,Ran He,&Kaiqi Huang.(2017).Deep Aesthetic Quality Assessment With Semantic Information.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(3),1482-1495.
MLA Yueying Kao,et al."Deep Aesthetic Quality Assessment With Semantic Information".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.3(2017):1482-1495.
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