  In this paper, we present PARTICUL, a novel algorithm for unsupervised
learning of part detectors from datasets used in fine-grained recognition. It
exploits the macro-similarities of all images in the training set in order to
mine for recurring patterns in the feature space of a pre-trained convolutional
neural network. We propose new objective functions enforcing the locality and
unicity of the detected parts. Additionally, we embed our detectors with a
confidence measure based on correlation scores, allowing the system to estimate
the visibility of each part. We apply our method on two public fine-grained
datasets (Caltech-UCSD Bird 200 and Stanford Cars) and show that our detectors
can consistently highlight parts of the object while providing a good measure
of the confidence in their prediction. We also demonstrate that these detectors
can be directly used to build part-based fine-grained classifiers that provide
a good compromise between the transparency of prototype-based approaches and
the performance of non-interpretable methods.
