Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88344
Title: Comparing humans to automation in rating photographic aesthetics
Authors: Kakarala, Ramakrishna
Agrawal, Abhishek
Morales, Sandino
Keywords: Photography
DRNTU::Engineering::Computer science and engineering
Aesthetics
Issue Date: 2015
Source: Kakarala, R., Agrawal, A., & Morales, S. (2015). Comparing humans to automation in rating photographic aesthetics. Proceedings of SPIE - Imaging and Multimedia Analytics in a Web and Mobile World 2015, 9408, 94080C-. doi:10.1117/12.2084991
Abstract: Computer vision researchers have recently developed automated methods for rating the aesthetic appeal of a photograph. Machine learning techniques, applied to large databases of photos, mimic with reasonably good accuracy the mean ratings of online viewers. However, owing to the many factors underlying aesthetics, it is likely that such techniques for rating photos do not generalize well beyond the data on which they are trained. This paper reviews recent attempts to compare human ratings, obtained in a controlled setting, to ratings provided by machine learning techniques. We review methods to obtain meaningful ratings both from selected groups of judges and also from crowd sourcing. We find that state-of-the-art techniques for automatic aesthetic evaluation are only weakly correlated with human ratings. This shows the importance of obtaining data used for training automated systems under carefully controlled conditions.
URI: https://hdl.handle.net/10356/88344
http://hdl.handle.net/10220/46915
DOI: http://dx.doi.org/10.1117/12.2084991
Rights: © 2015 Society of Photo-optical Instrumentation Engineers (SPIE). This paper was published in Proceedings of SPIE - Imaging and Multimedia Analytics in a Web and Mobile World 2015 and is made available as an electronic reprint (preprint) with permission of Society of Photo-optical Instrumentation Engineers (SPIE). The published version is available at: [http://dx.doi.org/10.1117/12.2084991]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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