Classification of segmented objects through a multi-net approach

Authors:
Alessandro Zamberletti;Ignazio Gallo;Simone Albertini;Marco Vanetti;Angelo Nodari
Affiliations:
Dipartimento di Scienze Teoriche ed Applicate, University of Insubria, Varese, Italy;Dipartimento di Scienze Teoriche ed Applicate, University of Insubria, Varese, Italy;Dipartimento di Scienze Teoriche ed Applicate, University of Insubria, Varese, Italy;Dipartimento di Scienze Teoriche ed Applicate, University of Insubria, Varese, Italy;Dipartimento di Scienze Teoriche ed Applicate, University of Insubria, Varese, Italy
Venue:
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Year:
2012

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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.