Abstract Qualitative Portrait Classification
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@MISC{Albuquerque_abstractqualitative,
author = {Georgia Albuquerque and Timo Stich and Marcus Magnor},
title = {Abstract Qualitative Portrait Classification},
year = {}
}
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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. 1
Keyphrases
abstract qualitative portrait classification selection process digital photography portrait image time consuming automatic pre-selection satisfying outcome large set new level portrait situation possible advantage recent advance high-quality digital photography large series short training period human subject small number
