Training Data Collection System for a Learning-based Photographic Aesthetic Quality Inference Engine
Razvan Orendovici and James Z. Wang
The Pennsylvania State University
We present a novel data collection system deployed for the ACQUINE -
Aesthetic Quality Inference Engine. The goal of the system is to
collect online user opinions, both structured and unstructured, for
training future generation learning-based aesthetic quality inference
engines. The development of the system was based on an analysis of
over 60,000 user comments of photographs. For photos processed and
rated by our engine, all users are invited to provide manual ratings.
The users can also choose up to three key photographic features that
the user liked, from a list, or to add features not in the list.
Within a few months that the system is available for public use,
more than 20,000 photos have received manual ratings and key
features for over 1,800 photos have been identified. We expect the
data generated over time will be critical in the study of
computational inferencing of visual aesthetics in photographs. The
system is demonstrated at http://acquine.alipr.com.
Full color PDF file (3 MB)
Razvan Orendovici and James Z. Wang, ``Training Data Collection System
for a Learning-based Photographic Aesthetic Quality Inference
Engine,'' Proceedings of the ACM International Conference on
Multimedia, Demonstration, pp. 1575-1578,
Florence, Italy, ACM, October 2010.
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July 23, 2010