Training data collection system for a learning-based photographic aesthetic quality inference engine

Razvan Orendovici, James Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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 used 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

Original languageEnglish (US)
Title of host publicationMM'10 - Proceedings of the ACM Multimedia 2010 International Conference
Pages1575-1578
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10 - Firenze, Italy
Duration: Oct 25 2010Oct 29 2010

Publication series

NameMM'10 - Proceedings of the ACM Multimedia 2010 International Conference

Other

Other18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10
CountryItaly
CityFirenze
Period10/25/1010/29/10

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

Fingerprint Dive into the research topics of 'Training data collection system for a learning-based photographic aesthetic quality inference engine'. Together they form a unique fingerprint.

Cite this