Nonparametric scene parsing: Label transfer via dense scene alignment
Author(s)Torralba, Antonio; Liu, Ce; Yuen, Jenny
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009
Institute of Electrical and Electronics Engineers
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.
Final published version
INSPEC Accession Number: 10836097