Segmenting Scenes by Matching Image Composites
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3718-segmenting-scenes-by-matching-image-composites.pdf
Description
Published version
Size
848.13 KB
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Unknown
Checksum (MD5)
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Author(s)
Russell, Bryan C.
Efros, Alexei A.
Sivic, Josef
Freeman, William T
Zisserman, Andrew
Date Issued
December 2009
Journal
NIPS'09: Proceedings of the 22nd International Conference on Neural Information Processing Systems
Publisher
ACM
Citation
Russell, Bryan C., Efros, Alexei A., Sivic, Josef, Freeman, William T. and Zisserman, Andrew. 2009. "Segmenting Scenes by Matching Image Composites."
Version
Final published version
Abstract
In this paper, we investigate how, given an image, similar images sharing the same global description can help with unsupervised scene segmentation. In contrast to recent work in semantic alignment of scenes, we allow an input image to be explainedby partial matches of similar scenes. This allows for a better explanation of the input scenes. We perform MRF-based segmentation that optimizes over matches, while respecting boundary information. The recovered segments are then used to re-query a large database of images to retrieve better matches for the target regions. We show improved performance in detecting the principal occluding and contact boundaries for the scene over previous methods on data gathered from the LabelMe database.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/3718-segmenting-scenes-by-matching-image-composites
