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Proceedings Paper

Content-aware automatic cropping for consumer photos
Author(s): Hao Tang; Daniel Tretter; Qian Lin
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Paper Abstract

Consumer photos are typically authored once, but need to be retargeted for reuse in various situations. These include printing a photo on different size paper, changing the size and aspect ratio of an embedded photo to accommodate the dynamic content layout of web pages or documents, adapting a large photo for browsing on small displays such as mobile phone screens, and improving the aesthetic quality of a photo that was badly composed at the capture time. In this paper, we propose a novel, effective, and comprehensive content-aware automatic cropping (hereafter referred to as “autocrop”) method for consumer photos to achieve the above purposes. Our autocrop method combines the state-of-the-art context-aware saliency detection algorithm, which aims to infer the likely intent of the photographer, and the “branch-and-bound” efficient subwindow search optimization technique, which seeks to locate the globally optimal cropping rectangle in a fast manner. Unlike most current autocrop methods, which can only crop a photo into an arbitrary rectangle, our autocrop method can automatically crop a photo into either a rectangle of arbitrary dimensions or a rectangle of the desired aspect ratio specified by the user. The aggressiveness of the cropping operation may be either automatically determined by the method or manually indicated by the user with ease. In addition, our autocrop method is extended to support the cropping of a photo into non-rectangular shapes such as polygons of any number of sides. It may also be potentially extended to return multiple cropping suggestions, which will enable the creation of new photos to enrich the original photo collections. Our experimental results show that the proposed autocrop method in this paper can generate high-quality crops for consumer photos of various types.

Paper Details

Date Published: 21 March 2013
PDF: 15 pages
Proc. SPIE 8664, Imaging and Printing in a Web 2.0 World IV, 86640C (21 March 2013); doi: 10.1117/12.2009058
Show Author Affiliations
Hao Tang, Hewlett-Packard Labs. (United States)
Daniel Tretter, Hewlett-Packard Labs. (United States)
Qian Lin, Hewlett-Packard Labs. (United States)

Published in SPIE Proceedings Vol. 8664:
Imaging and Printing in a Web 2.0 World IV
Qian Lin; Jan P. Allebach; Zhigang Fan, Editor(s)

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