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ABSTRACT
We present a non-photorealistic algorithm for retargeting large images to small size displays, particularly on mobile devices. This method adapts large images so that important objects in the image are still recognizable when displayed at a lower target resolution. Existing image manipulation techniques such as cropping works well for images containing a single important object, and down-sampling works well for images containing low frequency information. However, when these techniques are automatically applied to images with multiple objects, the image quality degrades and important information may be lost. Our algorithm addresses the case of multiple important objects in an image. The retargeting algorithm segments an image into regions, identifies important regions, removes them, fills the resulting gaps, resizes the remaining image, and re-inserts the important regions. Our approach lies in constructing a topologically constrained epitome of an image based on a visual attention model that is both comprehensible and size varying, making the method suitable for display-critical applications.
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CITED BY 11
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Amy Hwang , Shane Ahern , Simon King , Mor Naaman , Rahul Nair , Jeannie Yang, Zurfer: mobile multimedia access in spatial, social and topical context, Proceedings of the 15th international conference on Multimedia, September 25-29, 2007, Augsburg, Germany
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Stephan Kopf , Thomas King , Philipp Bostan , Hendrik Lemelson , Sina Deibert , Wolfgang Effelsberg, Fundamental services for context-sensitive mobile applications, Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, July 21-25, 2008, Dublin, Ireland
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