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Automatic discovery of discriminative parts as a quadratic assignment problem

Ronan Sicre 1, 2 Julien Rabin 3 Yannis Avrithis 2 Teddy Furon 2 Frédéric Jurie 3 Ewa Kijak 2
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
3 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Part-based image classification consists in representing categories by small sets of discriminative parts upon which a representation of the images is built. This paper addresses the question of how to automatically learn such parts from a set of labeled training images. We propose to cast the training of parts as a quadratic assignment problem in which optimal correspondences between image regions and parts are automatically learned. The paper analyses different assignment strategies and thoroughly evaluates them on two public datasets: Willow actions and MIT 67 scenes.
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Submitted on : Wednesday, October 25, 2017 - 9:51:56 AM
Last modification on : Friday, January 8, 2021 - 3:39:55 AM
Long-term archiving on: : Friday, January 26, 2018 - 12:39:55 PM


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  • HAL Id : hal-01623148, version 1


Ronan Sicre, Julien Rabin, Yannis Avrithis, Teddy Furon, Frédéric Jurie, et al.. Automatic discovery of discriminative parts as a quadratic assignment problem. ICCV Workshops -- CEFRL, Oct 2017, Venise, Italy. pp.1059-1068. ⟨hal-01623148⟩



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