Local Convolutional Features with Unsupervised Training for Image Retrieval

Mattis Paulin 1 Matthijs Douze 1 Zaid Harchaoui 1 Julien Mairal 1 Florent Perronnin 2 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 Xerox Research Centre Europe [Meylan]
Abstract : Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval. We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval. The proposed family of descriptors, called Patch-CKN, adapt the recently introduced Convolutional Kernel Network (CKN), an unsupervised framework to learn convolutional architectures. We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel ``RomePatches'' dataset. Patch-CKN descriptors yield competitive results compared to supervised CNNs alternatives on patch and image retrieval.
Type de document :
Communication dans un congrès
ICCV 2015 - IEEE International Conference on Computer Vision, Dec 2015, Santiago, Chile. IEEE, pp.91-99, <10.1109/ICCV.2015.19>
Liste complète des métadonnées


figure_deep_kernel_2layer.jpg
figure_deep_kernel_2layer.jpg
https://hal.inria.fr/hal-01207966
Contributeur : Thoth Team <>
Soumis le : jeudi 1 octobre 2015 - 16:27:12
Dernière modification le : jeudi 9 février 2017 - 17:01:57
Document(s) archivé(s) le : samedi 2 janvier 2016 - 11:20:59

Fichiers

deep_patches.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | LJK | UGA | LJK_GI | LJK_GI_LEAR

Citation

Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronnin, et al.. Local Convolutional Features with Unsupervised Training for Image Retrieval. ICCV 2015 - IEEE International Conference on Computer Vision, Dec 2015, Santiago, Chile. IEEE, pp.91-99, <10.1109/ICCV.2015.19>. <hal-01207966>

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de
la notice

1239

Téléchargements du document

9955

Contact


archive-ouverte@inria.fr