A proposal of adaptive probabilistic model of context applied to visual recognition

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dc.contributor.authorPeralta Márquez, Billy
dc.contributor.authorVergaray Illescas, Norman
dc.contributor.authorCaro Saldivia, Luis
dc.contributor.authorIEEE
dc.date2018
dc.date.accessioned2021-04-30T16:35:24Z
dc.date.available2021-04-30T16:35:24Z
dc.description.abstractWithin the increasing automation of tasks, it is necessary to obtain relevant information from images, to have clarity of the actions that must be performed. In this process, it is possible to detect objects individually, however this mode can generate a considerable error due to the enormous number of ways in which an object can be presented. It is therefore necessary to improve the level of accuracy, and a way to achieve this is taking into account the relationships between objects as well. Until now, context-oriented models generate relationships between objects without discriminating between input images. It is necessary that the model operates on each particular image, to reduce the error and obtain a conclusive result. In response to the above, this work proposes an adaptive probabilistic context-model. The model in question is a functional that processes the occurrence probability of the objects in each image, and generates the relations between the detectable objects using a Bayesian network in the form of a tree. This model is updated with the features of each image, requiring a minimization of the quadratic error through a numerical approximation, obtained by Newton-Raphson method. Comparisons were made between different heuristic proposals, as well as tests on different context-oriented databases, in order to validate the results. On the other hand, tests were performed using features extracted through histograms of orient gradients and Deep Learning. It was found that an improvement in the prediction of the order of 20% is feasible in the testing process.
dc.identifier.citation2018 37TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC),Vol.,,2018
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/3186
dc.language.isoen
dc.publisherIEEE
dc.source2018 37TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC)
dc.subject.englishObject recognition
dc.subject.englishBayesian networks
dc.subject.englishFunctional optimization
dc.titleA proposal of adaptive probabilistic model of context applied to visual recognition
dc.typeMeeting
uct.catalogadorWOS
uct.indizacionISTP
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