HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [42 references]  Display  Hide  Download

Contributor : Frederic Jurie Connect in order to contact the contributor
Submitted on : Wednesday, October 25, 2017 - 9:51:56 AM
Last modification on : Friday, April 8, 2022 - 4:08:03 PM
Long-term archiving on: : Friday, January 26, 2018 - 12:39:55 PM


Files produced by the author(s)


  • 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⟩



Record views


Files downloads