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Nonparametric Scene Parsing via Label Transfer (2011)
| Citations: | 66 - 3 self |
BibTeX
@MISC{Liu11nonparametricscene,
author = {Ce Liu and Jenny Yuen and Antonio Torralba},
title = {Nonparametric Scene Parsing via Label Transfer},
year = {2011}
}
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Abstract
While there has been a lot of recent work on object recognition and image understanding, the focus has been on carefully establishing mathematical models for images, scenes, and objects. In this paper, we propose a novel, nonparametric approach for object recognition and scene parsing using a new technology we name label transfer. For an input image, our system first retrieves its nearest neighbors from a large database containing fully annotated images. Then, the system establishes dense correspondences between the input image and each of the nearest neighbors using the dense SIFT flow algorithm [28], which aligns two images based on local image structures. Finally, based on the dense scene correspondences obtained from 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 challenging databases. Compared to existing object recognition approaches that require training classifiers or appearance models for each object category, our system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.
Keyphrases
label transfer nonparametric scene parsing input image object recognition object category appearance model local image structure recent work query image image understanding dense scene correspondence new technology contextual information large database sift flow nonparametric scene multiple cue retrieval alignment procedure mathematical model object recognition approach dense correspondence markov random field framework dense sift flow algorithm nonparametric approach experimental result
