Abstract. This paper presents an approach to object discovery in a given unlabeled image set, based on mining repetitive spatial configurations of image contours. Contours that si...
Abstract. We present algorithmic lower bounds on the size of the largest independent sets of vertices in a random d-regular graph. Our bounds hold with probability approaching one ...
We analyze the convergence of the spectrum of large random graphs to the spectrum of a limit infinite graph. We apply these results to graphs converging locally to trees and deri...
Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...
Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high ...