Open AccessStrategy for aesthetic photography recommendation via collaborative composition model
Author(s) -
Zhang Yanhao,
Huang Qingming,
Qin Lei,
Zhao Sicheng,
Lu Xiusheng,
Sun Xiaoshuai,
Yao Hongxun
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0276
Subject(s) - composition (language) , representation (politics) , photography , computer science , scheme (mathematics) , encode , artificial intelligence , computer vision , square (algebra) , exploit , portrait , visual arts , mathematics , art , mathematical analysis , biochemistry , chemistry , geometry , literature , computer security , politics , political science , law , gene
In this study, the authors propose a collaborative composition model for automatically recommending suitable positions and poses in the scene of photography taken by amateurs. By analysing aesthetic‐aware features, the authors' strategy jointly takes attention and geometry composition into account to learn the aesthetic manifestation knowledge of professional photographers. Firstly, aesthetic composition representation exploits the strength of visual saliency to explicitly encode the spatial correlation of the professional photos. Secondly, ℓ2 regularised least square is adopted to constrain the representation coefficients, which provides a fast solution in selecting aesthetic candidates collaboratively. In addition, a novel confidence measure scheme is further designed based on reconstruction errors and the reference photos are updated adaptively according to the composition rules. Both qualitative and quantitative evaluations show that the model performs well for the portrait photographing recommendation.