CNN features are also great at unsupervised classification
This work provides insight into the transferability of deep features to unsupervised tasks, with applications in robotics for object sorting.
The paper tackled the problem of image set clustering for object and fine-grained classification by using pretrained CNN features with classic clustering algorithms, achieving better results than state-of-the-art methods.
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.