Nonparametric scene parsing: Label transfer via dense scene alignment
Name
Liu-2009-Nonparametric scene parsing Label transfer via dense scene alignment.pdf
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4.36 MB
Format
Adobe PDF
Checksum (MD5)
caa37fe832dc530b54d88b7fe5fde94c
Author(s)
Torralba, Antonio
Liu, Ce
Yuen, Jenny
Date Issued
August 2009
Journal
IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Ce Liu, J. Yuen, and A. Torralba. “Nonparametric scene parsing: Label transfer via dense scene alignment.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 2009. 1972-1979. © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
In this paper we propose a novel nonparametric approach for object recognition and scene parsing using dense scene alignment. Given an input image, we retrieve its best matches from a large database with annotated images using our modified, coarse-to-fine SIFT flow algorithm that aligns the structures within two images. Based on the dense scene correspondence obtained from the SIFT flow, our system warps the existing annotations, and integrates multiple cues in a Markov random field framework to segment and recognize the query image. Promising experimental results have been achieved by our nonparametric scene parsing system on a challenging database. Compared to existing object recognition approaches that require training for each object category, our system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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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://doi.org/10.1109/CVPRW.2009.5206536
