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.

Keywords

Computer scienceArtificial intelligenceParsingScale-invariant feature transformConditional random fieldPattern recognition (psychology)Cognitive neuroscience of visual object recognitionObject (grammar)Computer visionNonparametric statisticsMarkov random fieldTransfer of learningObject detectionImage retrievalImage (mathematics)Image segmentationMathematics

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Publication Info

Year
2011
Type
article
Volume
33
Issue
12
Pages
2368-2382
Citations
387
Access
Closed

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Cite This

Ce Liu, J. Yuen, Antonio Torralba (2011). Nonparametric Scene Parsing via Label Transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence , 33 (12) , 2368-2382. https://doi.org/10.1109/tpami.2011.131

Identifiers

DOI
10.1109/tpami.2011.131