Unsupervised Multi-Class Object Discovery By Spherical Clustering Of Deep Features

In this paper, we propose a novel method for multi-class co-localization; it offers unsupervised localization of the main object in each image from an image set containing multiple kinds of main objects. Our method utilizes deep features to tackle the co-localization problem. Deep features, which can be extracted by pre-trained neural networks, are effective against unsupervised co-localization from multi-class image set. Based on spherical clustering, we classify deep features into several clusters, and choose one dominant cluster for each image or each image set. Experiments show that this very simple approach is significantly better than conventional state-of-the-art techniques in terms of localization accuracy. Moreover, multi-class co-localization experiments show that our method has the potential to classify the object in each image at the same time as achieving localization.