Unsupervised Visual Object Categorisation with BoF and Spatial Matching

The ultimate challenge of image categorisation is unsupervised object discovery, where the selection of categories and the assignments of given images to these categories are performed automatically. The unsupervised setting prohibits the use of the best discriminative methods, and in Tuytelaars et al. [30] the standard Bag-of-Features (BoF) approach performed the best. The downside of the BoF is that it omits spatial information of local features. In this work, we propose a novel unsupervised image categorisation method which uses the BoF to find initial matches for each image (pre-filter) and then refines and ranks them using spatial matching of local features. Unsupervised visual object discovery is performed by the normalised cuts algorithm which produces the clusterings from a similarity matrix representing the spatial match scores. In our experiments, the proposed approach outperforms the best method in Tuytelaars et al with the Caltech-101, randomised Caltech-101, and Caltech-256 data sets. Especially for a large number of classes, clear and statistically significant improvements are achieved.