Unsupervised detection of regions of interest using iterative link analysis
Name
Torralba_Unsupervised detection.pdf
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4.63 MB
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Adobe PDF
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Author(s)
Kim, Gunhee
Torralba, Antonio
Date Issued
December 2009
Journal
Papers of the 23rd Annual Conference on Neural Information Processing Systems 2009, NIPS 2009
Publisher
Neural Information Processing Systems Foundation
Citation
Kim, Gunhee and Antonio Torralba. "Unsupervised Detection of Regions of Interest Using Iterative Link Analysis." Papers of the 23rd Annual Conference on Neural Information Processing Systems 2009, December 7-10, 2009, Vancouver, B.C., Canada.
Version
Author's final manuscript
Abstract
This paper proposes a fast and scalable alternating optimization technique to detect
regions of interest (ROIs) in cluttered Web images without labels. The proposed
approach discovers highly probable regions of object instances by iteratively
repeating the following two functions: (1) choose the exemplar set (i.e. a
small number of highly ranked reference ROIs) across the dataset and (2) refine
the ROIs of each image with respect to the exemplar set. These two subproblems
are formulated as ranking in two different similarity networks of ROI hypotheses
by link analysis. The experiments with the PASCAL 06 dataset show that our
unsupervised localization performance is better than one of state-of-the-art techniques
and comparable to supervised methods. Also, we test the scalability of our
approach with five objects in Flickr dataset consisting of more than 200K images.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
http://books.nips.cc/papers/files/nips22/NIPS2009_0004.pdf
