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IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 151–162Cite as

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  2. Artificial Neural Networks in Pattern Recognition
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Classification of Segmented Objects through a Multi-net Approach

Classification of Segmented Objects through a Multi-net Approach

  • Alessandro Zamberletti22,
  • Ignazio Gallo22,
  • Simone Albertini22,
  • Marco Vanetti22 &
  • …
  • Angelo Nodari22 
  • Conference paper
  • 1245 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7477)

Abstract

The proposed model aims to extend the MNOD algorithm adding a new type of node specialized in object classification. For each potential object identified by the MNOD, a set of segments are generated using a min-cut based algorithm with different seeds configurations. These segments are classified by a suitable neural model and then the one with higher value is chosen, in agreement with a proper energy function. The proposed method allows to segment and classify each object simultaneously. The results showed in the experiment section highlight the potential and the cost of having unified segmentation and classification in a single model.

Keywords

  • object segmentation
  • object classification
  • neural networks
  • minimum cut

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Teoriche ed Applicate, University of Insubria, via Mazzini 5, Varese, Italy

    Alessandro Zamberletti, Ignazio Gallo, Simone Albertini, Marco Vanetti & Angelo Nodari

Authors
  1. Alessandro Zamberletti
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  2. Ignazio Gallo
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  3. Simone Albertini
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  4. Marco Vanetti
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  5. Angelo Nodari
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Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Zamberletti, A., Gallo, I., Albertini, S., Vanetti, M., Nodari, A. (2012). Classification of Segmented Objects through a Multi-net Approach. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_14

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  • DOI: https://doi.org/10.1007/978-3-642-33212-8_14

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  • Print ISBN: 978-3-642-33211-1

  • Online ISBN: 978-3-642-33212-8

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