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dc.contributor.authorTorralba, Antonioen_US
dc.contributor.authorMurphy, Kevin P.en_US
dc.contributor.authorFreeman, William T.en_US
dc.date.accessioned2004-10-08T20:43:16Z
dc.date.available2004-10-08T20:43:16Z
dc.date.issued2004-06-25en_US
dc.identifier.otherAIM-2004-013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6740
dc.description.abstractWe seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.en_US
dc.format.extent10 p.en_US
dc.format.extent2184856 bytes
dc.format.extent906515 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-2004-013en_US
dc.subjectAIen_US
dc.subjectObject detectionen_US
dc.subjectcontexten_US
dc.subjectboostingen_US
dc.subjectBPen_US
dc.subjectrandom fieldsen_US
dc.titleContextual models for object detection using boosted random fieldsen_US


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