CASIA OpenIR
Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping
Li, Debang1,2; Wu, Huikai1,2; Zhang, Junge1,2; Huang, Kaiqi1,2,3
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-10-01
Volume28Issue:10Pages:5105-5120
Corresponding AuthorHuang, Kaiqi(kqhuang@nlpr.ia.ac.cn)
AbstractImage cropping aims at improving the quality of images by removing unwanted outer areas, which is widely used in the photography and printing industry. Most of the previous cropping methods that do not need bounding box supervision rely on the sliding window mechanism. The sliding window method results in fixed aspect ratios and limits the shape of the cropping region. Moreover, the sliding window method usually produces lots of candidates on the input image, which is very time-consuming. Motivated by these challenges, we formulate image cropping as a sequential decision-making process and propose a reinforcement learning-based framework to address this problem, namely, Fast Aesthetics-Aware Adversarial Reinforcement Learning (Fast A3RL). Particularly, the proposed method develops an aesthetics-aware reward function that is dedicated for image cropping. Similar to human's decision-making process, we use a comprehensive state representation, including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The adversarial learning process is also applied during the training stage. The proposed method is evaluated on several popular cropping datasets, in which the images are unseen during training. The experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with related methods.
KeywordReinforcement learning adversarial learning image cropping
DOI10.1109/TIP.2019.2914360
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1001004] ; National Key Research and Development Program of China[2016YFB1001005] ; National Natural Science Foundation of China[61876181] ; National Natural Science Foundation of China[61673375] ; National Natural Science Foundation of China[61721004] ; Projects of Chinese Academy of Sciences[QYZDB-SSW-JSC006]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Projects of Chinese Academy of Sciences
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000482599100009
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27336
Collection中国科学院自动化研究所
Corresponding AuthorHuang, Kaiqi
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Debang,Wu, Huikai,Zhang, Junge,et al. Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(10):5105-5120.
APA Li, Debang,Wu, Huikai,Zhang, Junge,&Huang, Kaiqi.(2019).Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(10),5105-5120.
MLA Li, Debang,et al."Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.10(2019):5105-5120.
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