Personal consumer photography collections often contain photos captured by numerous devices stored both locally and via online services. The task of gathering, organizing, and assembling still and video assets in preparation for sharing with others can be quite challenging. Current commercial photobook applications are mostly manual-based requiring significant user interactions. To assist the consumer in organizing these assets, we propose an automatic method to assign a fitness score to each asset, whereby the top scoring assets are used for product creation. Our method uses cues extracted from analyzing pixel data, metadata embedded in the file, as well as ancillary tags or online comments. When a face occurs in an image, its features have a dominating influence on both aesthetic and compositional properties of the displayed image. As such, this paper will emphasize the contributions faces have on affecting the overall fitness score of an image. To understand consumer preference, we conducted a psychophysical study that spanned 27 judges, 5,598 faces, and 2,550 images. Preferences on a per-face and per-image basis were independently gathered to train our classifiers. We describe how to use machine learning techniques to merge differing facial attributes into a single classifier. Our novel methods of facial weighting, fusion of facial attributes, and dimensionality reduction produce stateof- the-art results suitable for commercial applications.

Date Published: 7 March 2014
PDF: 9 pages
Proc. SPIE 9020, Computational Imaging XII, 90200C (7 March 2014); doi: 10.1117/12.2040393

Published in SPIE Proceedings Vol. 9020:
Computational Imaging XII
Charles A. Bouman; Ken D. Sauer, Editor(s)