Systems And Methods For Retrieving Causal Sets Of Events From Unstructured Signals

dc.contributor.patentcreator Rehg, James M.
dc.contributor.patentcreator Prabhakar, Karthir
dc.contributor.patentcreator Oh, Sangmin
dc.contributor.patentcreator Wang, Ping
dc.contributor.patentcreator Abowd, Gregory D.
dc.date.accessioned 2017-05-12T14:26:31Z
dc.date.available 2017-05-12T14:26:31Z
dc.date.filed 3/22/2012
dc.date.issued 12/9/2014
dc.description.abstract A method for providing improved performance in retrieving and classifying causal sets of events from an unstructured signal can comprise applying a temporal-causal analysis to the unstructured signal. The temporal-causal analysis can comprise representing the occurrence times of visual events from an unstructured signal as a set of point processes. An exemplary embodiment can comprise interpreting a set of visual codewords produced by a space-time-dictionary representation of the unstructured video sequence as the set of point processes. A nonparametric estimate of the cross-spectrum between pairs of point processes can be obtained. In an exemplary embodiment, a spectral version of the pairwise test for Granger causality can be applied to the nonparametric estimate to identify patterns of interactions between visual codewords and group them into semantically meaningful independent causal sets. The method can further comprise leveraging the segmentation achieved during temporal causal analysis to improve performance in categorizing causal sets.
dc.description.assignee Georgia Tech Research Corporation
dc.identifier.cpc G06K9/00718
dc.identifier.patentapplicationnumber 13/427610
dc.identifier.patentnumber 8909025
dc.identifier.uri http://hdl.handle.net/1853/56946
dc.identifier.uspc 386/241
dc.title Systems And Methods For Retrieving Causal Sets Of Events From Unstructured Signals
dc.type Text
dc.type.genre Patent
dspace.entity.type Publication
local.contributor.corporatename Georgia Institute of Technology
local.relation.ispartofseries Georgia Tech Patents
relation.isOrgUnitOfPublication cc30e153-7a64-4ae2-9b1d-5436686785e3
relation.isSeriesOfPublication 0f49c79d-4efb-4bd9-b060-5c7f9191b9da
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