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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
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image 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.
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Submitted on : Thursday, April 13, 2017 - 9:32:28 AM
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  • HAL Id : hal-01507379, version 1
  • ARXIV : 1704.03755

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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⟩

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