Computer Graphics
TU Braunschweig

Qualitative Portrait Classification

Qualitative Portrait Classification

Due to recent advances in high-quality digital photography, taking a large series of images is very inexpensive. Especially in portrait situations, this results in a possible advantage because subjects often feel uncomfortable during acquisition. Selecting from a larger set of images increases the chance of a more satisfying outcome. However, the selection process is not easy and time consuming as only a small number of images is typically considered as aesthetically pleasing. In this work, we propose a machine learning approach to mimic the selection process of a human subject. After a short training period, a large set of images can be classified instantly into two categories, good or bad. With the

proposed automatic pre-selection, the advantage of digital photography for portrait images is brought to a new level.

Author(s):Georgia Albuquerque, Timo Stich, Marcus Magnor
Published:November 2007
Type:Article in conference proceedings
Book:Proc. Vision, Modeling and Visualization (VMV)
Presented at:Vision, Modeling and Visualization (VMV)

  title = {Qualitative Portrait Classification},
  author = {Albuquerque, Georgia and Stich, Timo and Magnor, Marcus},
  booktitle = {Proc. Vision, Modeling and Visualization ({VMV})},
  organization = {Eurographics},
  editor = {H.P.A. Lensch and B. Rosenhahn and H.-P. Seidel and P. Slusallek and J. Weickert},
  pages = {243--252},
  month = {Nov},
  year = {2007}