DocumentCode
3413018
Title
Aesthetic composition represetation for portrait photographing recommendation
Author
Yanhao Zhang ; Xiaoshuai Sun ; Hongxun Yao ; Lei Qin ; Qingming Huang
Author_Institution
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
fYear
2012
fDate
Sept. 30 2012-Oct. 3 2012
Firstpage
2753
Lastpage
2756
Abstract
In this paper, we present an intelligent portrait photographing framework for automatically recommending the suitable positions and poses in the scene of photography taken by amateurs. By analyzing aesthetic characteristics features, we propose a solution by constructing aesthetic composition representation which covers the attention composition and geometry composition to identify the underlying technique of professional photographer. First, we extract the attention composition feature of the professional photo by utilizing a visual saliency model. Then, a geometry composition feature is also presented to learn the spatial correlation. Finally, composition rules are applied to make appropriate pose and position. Experiments show our aesthetic composition representation performs well for portrait photographing recommendation.
Keywords
feature extraction; geometry; image representation; natural scenes; recommender systems; aesthetic characteristics features; aesthetic composition representation; attention composition feature extraction; composition rules; geometry composition feature; intelligent portrait photographing recommendation; photography scene; professional photo; spatial correlation; visual saliency model; Context; Feature extraction; Geometry; Image segmentation; Photography; Quality assessment; Visualization; Aesthetic Recommendation; Attention and Geometry Composition; Composition Rules;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location
Orlando, FL
ISSN
1522-4880
Print_ISBN
978-1-4673-2534-9
Electronic_ISBN
1522-4880
Type
conf
DOI
10.1109/ICIP.2012.6467469
Filename
6467469
Link To Document