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Proceedings Paper

Automatic image selection by means of a hierarchical scalable collection representation
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Paper Abstract

This paper presents a system for automatic image selection for storytelling applications, like slideshows and photobooks, where targeting a specific image count is usually of high importance. A versatile image collection representation is introduced, which allows for automatic scalable selection in order to target a specific final image count, while preserving a good coverage of the event in order to maintain the storytelling potential of the selection. A hierarchical time clustering is presented, which is traversed at a specific hierarchy level in order to select images by alternating among all time clusters, and selecting the most relevant images in that cluster. The relevance ordering we use is based on a combination of features, namely, important people, smile detection, image appeal measures, and whether a nearduplicate of the image has already been selected. Once this Hierarchical Scalable Representation has been created, it can be reused to generate any target size selection. Two automatic image selection algorithms have been implemented, one that selects images from clusters with high average image relevance more frequently, and another one that selects images from larger clusters more frequently. The overall system has been used over the last year on several large image collections; the resulting selection was presented to their owners in the form of photo-books in order to get feedback, validating the presented approach.

Paper Details

Date Published: 19 January 2009
PDF: 12 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570W (19 January 2009); doi: 10.1117/12.806088
Show Author Affiliations
Pere Obrador, Hewlett-Packard Labs. (United States)
Nathan Moroney, Hewlett-Packard Labs. (United States)

Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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