The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The amount of labeled training data required for image interpretation tasks is a major drawback of current methods. How can we use the gigantic collection of unlabeled images available on the web to aid these tasks? In this paper, we present a simple approach based on the notion of patch-based context to extract useful priors for regions within a query image from a large collection of (6 million) unlabeled images. This contextual prior over image classes acts as a non-redundant complimentary source of knowledge that helps in disambiguating the confusions within the predictions of local region-level features. We demonstrate our approach on the challenging tasks of region classification and surface layout estimation.

Notes

Text Reference

Santosh Kumar Divvala, Alexei A. Efros, Martial Hebert, and Svetlana Lazebnik, "Unsupervised Patch-based Context from Millions of Images," tech. report CMU-RI-TR-11-38, Robotics Institute, Carnegie Mellon University, December, 2011

BibTeX Reference

@techreport{Divvala_2011_6952,
   author = "Santosh Kumar Divvala and Alexei A. Efros and Martial Hebert and Svetlana Lazebnik",
   title = "Unsupervised Patch-based Context from Millions of Images",
   booktitle = "",
   institution = "Robotics Institute",
   month = "December",
   year = "2011",
   number= "CMU-RI-TR-11-38",
   address= "Pittsburgh, PA",
}

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