Open AccessReinforcement learning cropping method based on comprehensive feature and aesthetics assessment
Author(s) -
Zhang Yaqing,
Li Xueming,
Li Xuewei
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12420
Subject(s) - cropping , economic shortage , computer science , face (sociological concept) , image (mathematics) , artificial intelligence , feature (linguistics) , process (computing) , reinforcement learning , feature extraction , agricultural engineering , quality (philosophy) , pattern recognition (psychology) , computer vision , agriculture , engineering , social science , linguistics , philosophy , epistemology , government (linguistics) , sociology , operating system , ecology , biology
Automatic image cropping can change the composition to improve the aesthetic quality of the images. Most of the existing automatic image cropping methods based on specific features need to generate a large number of candidate cropping windows. It is very time‐consuming and can only produce a limited aspect ratio results. In the face of these situations, a reinforcement learning cropping method based on comprehensive feature and aesthetics assessment is proposed. It does not need to produce a large number of candidate windows. Its gradually cropping mode is more in line with the process of image cropping by human. What is more, the proposed method takes the image aesthetic assessment into consideration. Experimental results show that the proposed method improves the cropping efficiency and achieves excellent cropping effect on the open Flickr Cropping Dataset and CUHK Image Cropping Dataset. The proposed method can overcome the shortages of existing methods.