Skip to Main content Skip to Navigation
Conference papers

Unsupervised part learning for visual recognition

Ronan Sicre 1 Yannis Avrithis 1 Ewa Kijak 1 Frédéric Jurie 2
1 LinkMedia - Creating and exploiting explicit links between multimedia fragments
IRISA-D6 - MEDIA ET INTERACTIONS, Inria Rennes – Bretagne Atlantique
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promising avenue a decade ago, this direction has been neglected since the advent of deep neural networks. In this context, this paper brings two contributions: first, this work proceeds one step further compared to recent part-based models (PBM), focusing on how to learn parts without using any labeled data. Instead of learning a set of parts per class, as generally performed in the PBM literature, the proposed approach both constructs a partition of a given set of images into visually similar groups, and subsequently learns a set of discriminative parts per group in a fully unsu-pervised fashion. This strategy opens the door to the use of PBM in new applications where labeled data are typically not available, such as instance-based image retrieval. Second , this paper shows that despite the recent success of end-to-end models, explicit part learning can still boost classification performance. We experimentally show that our learned parts can help building efficient image representations , which outperform state-of-the art Deep Convolu-tional Neural Networks (DCNN) on both classification and retrieval tasks.
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01507379
Contributor : Frederic Jurie <>
Submitted on : Thursday, April 13, 2017 - 9:32:28 AM
Last modification on : Tuesday, February 25, 2020 - 8:08:12 AM

File

1704.03755.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01507379, version 1
  • ARXIV : 1704.03755

Citation

Ronan Sicre, Yannis Avrithis, Ewa Kijak, Frédéric Jurie. Unsupervised part learning for visual recognition. CVPR 2017 - IEEE Conference on Computer Vision and Pattern Recognition, Jul 2017, Honolulu, United States. ⟨hal-01507379⟩

Share

Metrics

Record views

587

Files downloads

420