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dc.contributor.authorHumayun, Ahmad
dc.contributor.authorLi, Fuxin
dc.contributor.authorRehg, James M.
dc.date.accessioned2015-07-10T19:34:15Z
dc.date.available2015-07-10T19:34:15Z
dc.date.issued2014-06
dc.identifier.citationHumayun, A.; Fuxin Li; Rehg, J.M. (2014). "RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014), 23-28 June 2014, pp. 336-343.en_US
dc.identifier.urihttp://hdl.handle.net/1853/53676
dc.description© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.descriptionDOI: 10.1109/CVPR.2014.50
dc.description.abstractPopular figure-ground segmentation algorithms generate a pool of boundary-aligned segment proposals that can be used in subsequent object recognition engines. These algorithms can recover most image objects with high accuracy, but are usually computationally intensive since many graph cuts are computed with different enumerations of segment seeds. In this paper we propose an algorithm, RIGOR, for efficiently generating a pool of overlapping segment proposals in images. By precomputing a graph which can be used for parametric min-cuts over different seeds, we speed up the generation of the segment pool. In addition, we have made design choices that avoid extensive computations without losing performance. In particular, we demonstrate that the segmentation performance of our algorithm is slightly better than the state-of-the-art on the PASCAL VOC dataset, while being an order of magnitude faster.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectBoostingen_US
dc.subjectGraph cutsen_US
dc.subjectObject proposalsen_US
dc.subjectObject segmentationen_US
dc.titleRIGOR: Reusing Inference in Graph Cuts for generating Object Regionsen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/CVPR.2014.50
dc.embargo.termsnullen_US


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