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CVPR
2009
IEEE

Nonparametric Scene Parsing: Label Transfer via Dense Scene Alignment

14 years 11 months ago
Nonparametric Scene Parsing: Label Transfer via Dense Scene Alignment
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.
Antonio B. Torralba, Ce Liu, Jenny Yuen
Added 05 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Antonio B. Torralba, Ce Liu, Jenny Yuen
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