• 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