Proceedings of the 2013 International Conference on Computer Graphics,Visualization, Computer Vision, and Game Technology

An Autonomous Aesthetics-driven Photographing Instructor with Personality Prediction

Authors
Chin-Shyurng Fahn, Meng-Luen Wu
Corresponding Author
Chin-Shyurng Fahn
Available Online December 2013.
DOI
https://doi.org/10.2991/visio-13.2014.3How to use a DOI?
Keywords
Computer vision, computa-tional aesthetics, autonomous photo-graphing instructor, personality prediction, social network.
Abstract
In this paper, an autonomous aesthetics-driven photographing instructor system is proposed, which gives instructions to help camera users to take good images. There are two kinds of instructions: image composition and personality feature enhancement. As for composition, a salient region is used to match the aesthetical template. To keep the personality of the autonomous photographing instructor system, the correlation between user types and image features is extracted through data mining on social networks, which is called “personality prediction.” The proposed system is run in realtime and workable on all mobile devices with cameras.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
The 2013 International Conference on Computer Graphics,Visualization, Computer Vision, and Game Technology
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
978-94-6252-002-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/visio-13.2014.3How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Chin-Shyurng Fahn
AU  - Meng-Luen Wu
PY  - 2013/12
DA  - 2013/12
TI  - An Autonomous Aesthetics-driven Photographing Instructor with Personality Prediction
BT  - The 2013 International Conference on Computer Graphics,Visualization, Computer Vision, and Game Technology
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/visio-13.2014.3
DO  - https://doi.org/10.2991/visio-13.2014.3
ID  - Fahn2013/12
ER  -