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dc.contributor.authorHumayun, Ahmad
dc.contributor.authorLi, Fuxin
dc.contributor.authorRehg, James M.
dc.date.accessioned2016-08-18T17:34:14Z
dc.date.available2016-08-18T17:34:14Z
dc.date.issued2015-12
dc.identifier.citationHumayun, A., Li, F., & Rehg, J. M. (2015). The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1600-1608.en_US
dc.identifier.urihttp://hdl.handle.net/1853/55478
dc.description© 2015 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/ICCV.2015.187en_US
dc.description.abstractObject proposals have recently fueled the progress in detection performance. These proposals aim to provide category-agnostic localizations for all objects in an image. One way to generate proposals is to perform parametric min-cuts over seed locations. This paper demonstrates that standard parametric-cut models are ineffective in obtaining medium-sized objects, which we refer to as the middle child problem. We propose a new energy minimization framework incorporating geodesic distances between segments which solves this problem. In addition, we introduce a new superpixel merging algorithm which can generate a small set of seeds that reliably cover a large number of objects of all sizes. We call our method POISE - "Proposals for Objects from Improved Seeds and Energies." POISE enables parametric min-cuts to reach their full potential. On PASCAL VOC it generates ~2,640 segments with an average overlap of 0.81, whereas the closest competing methods require more than 4,200 proposals to reach the same accuracy. We show detailed quantitative comparisons against 5 state-of-the-art methods on PASCAL VOC and Microsoft COCO segmentation challenges.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCategory-agnostic localizationsen_US
dc.subjectGeodesic distanceen_US
dc.subjectMiddle child problemen_US
dc.subjectObject proposalsen_US
dc.subjectParametric min-cut modelen_US
dc.subjectPOISEen_US
dc.subjectProposals for objects from improved seeds and energiesen_US
dc.titleThe Middle Child Problem: Revisiting Parametric Min-cut and Seeds for Object Proposalsen_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.corporatenameOregon State Universityen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineersen_US
dc.identifier.doi10.1109/ICCV.2015.187en_US


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