Qualitative Portrait Classification Georgia Albuquerque, Timo Stich, Marcus Magnor

Georgia Albuquerque, Timo Stich, and Marcus Magnor: "Qualitative Portrait Classification", in Proc. Vision, Modeling and Visualization (VMV), Saarbrücken, Germany, pp. 243–252, November 2007. [pdf] [bib]

Abstract

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.

TU Braunschweig - Fakultät für Mathematik und Informatik - Computer Graphics - Publications - Qualitative Portrait Classification