  This paper aims at providing insight on the transferability of deep CNN
features to unsupervised problems. We study the impact of different pretrained
CNN feature extractors on the problem of image set clustering for object
classification as well as fine-grained classification. We propose a rather
straightforward pipeline combining deep-feature extraction using a CNN
pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in
image-clustering and provides better results. These results strengthen the
belief that supervised training of deep CNN on large datasets, with a large
variability of classes, extracts better features than most carefully designed
engineering approaches, even for unsupervised tasks. We also validate our
approach on a robotic application, consisting in sorting and storing objects
smartly based on clustering.
