  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, it shows that despite the recent success of
end-to-end holistic models, explicit part learning can boosts classification
performance. Second, this work proceeds one step further than 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 done in the PBM
literature, the proposed approach both constructs a partition of a given set of
images into visually similar groups, and subsequently learn a set of
discriminative parts per group in a fully unsupervised fashion. This strategy
opens the door to the use of PBM in new applications for which the notion of
image categories is irrelevant, such as instance-based image retrieval, for
example. We experimentally show that our learned parts can help building
efficient image representations, for classification as well as for indexing
tasks, resulting in performance superior to holistic state-of-the art Deep
Convolutional Neural Networks (DCNN) encoding.
