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
Inria Rennes – Bretagne Atlantique , IRISA_D6 - MEDIA ET INTERACTIONS
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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Communication dans un congrès
ICCV Workshops -- CEFRL, Oct 2017, Venise, Italy. pp.1059-1068, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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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, 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). 〈hal-01623148〉

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