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Italian Workshop on Neural Nets

WIRN 2017 2017: Neural Advances in Processing Nonlinear Dynamic Signals pp 85–96Cite as

Context Analysis Using a Bayesian Normal Graph

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 102))

Abstract

Contextual information can be used to help object detection in video and images, or to categorize text. In this work we demonstrate how the Latent Variable Model, expressed as a Factor Graph in Reduced Normal Form, can manage contextual information to support a scene understanding task. In an unsupervised scenario our model learns how various objects can coexist, by associating object variables to a latent Bayesian cluster. The model, that is implemented using probabilistic message propagation, can be used to correct or to assign labels to new images.

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Authors and Affiliations

  1. Dipartimento di Ingegneria Industriale e dell’Informazione, Università della Campania “Luigi Vanvitelli” (ex SUN), via Roma 29, 81031, Aversa, CE, Italy

    Amedeo Buonanno, Paola Iadicicco, Giovanni Di Gennaro & Francesco A. N. Palmieri

Authors
  1. Amedeo Buonanno
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  2. Paola Iadicicco
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  3. Giovanni Di Gennaro
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  4. Francesco A. N. Palmieri
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Corresponding author

Correspondence to Amedeo Buonanno .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia, Università della Campana Luigi Vanvitelli, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataro, Barcelona, Spain

    Marcos Faundez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Laboratorio di Neuronica, Dipartimento Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Buonanno, A., Iadicicco, P., Di Gennaro, G., Palmieri, F.A.N. (2019). Context Analysis Using a Bayesian Normal Graph. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Neural Advances in Processing Nonlinear Dynamic Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 102. Springer, Cham. https://doi.org/10.1007/978-3-319-95098-3_8

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