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Modeling photo composition and its application to photo re-arrangement

Cited 29 time in Web of Science Cited 40 time in Scopus
Authors

Park, Jaesik; Lee, Joon-Young; Tai, Yu-Wing; Kweon, In So

Issue Date
2012
Publisher
ICIP
Citation
Proceedings - International Conference on Image Processing, ICIP, pp.2741-2744
Abstract
We introduce a learning based photo composition model and its application on photo re-arrangement. In contrast to previous approaches which evaluate quality of photo composition using the rule of thirds or the golden ratio, we train a normalized saliency map from visually pleasurable photos taken by professional photographers. We use Principal Component Analysis (PCA) to analyze training data and build a Gaussian mixture model (GMM) to describe the photo composition model. Our experimental results show that our approach is reliable and our trained photo composition model can be used to improve photo quality through photo re-arrangement. © 2012 IEEE.
ISSN
1522-4880
URI
https://hdl.handle.net/10371/201335
DOI
https://doi.org/10.1109/ICIP.2012.6467466
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  • College of Engineering
  • Dept. of Computer Science and Engineering
Research Area Computer Graphics, Computer Vision, Machine Learning, Robotics

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