Unsupervised detection of regions of interest using iterative link analysis
Author(s)Kim, Gunhee; Torralba, Antonio
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Papers of the 23rd Annual Conference on Neural Information Processing Systems 2009, NIPS 2009
Neural Information Processing Systems Foundation
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.
Author's final manuscript