In this study we propose sketching algorithms for computing similarities between hierarchical data. Specifically, we look at data objects that are represented using leaf-labeled t...
Gold’s original paper on inductive inference introduced a notion of an optimal learner. Intuitively, a learner identifies a class of objects optimally iff there is no other lea...
: A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a se...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
This article presents a general framework for integrating reasoning about object structure and concept taxonomies. The structural relations in the domain of objects discussed are ...
Unsupervised learning requires a grouping step that defines which data belong together. A natural way of grouping in images is the segmentation of objects or parts of objects. Whi...