The classification of graph based objects is an important challenge from a knowledge discovery standpoint and has attracted considerable attention recently. In this paper, we pres...
H. D. K. Moonesinghe, Hamed Valizadegan, Samah Jam...
Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
Graph edit distance provides an error-tolerant way to measure distances between attributed graphs. The effectiveness of edit distance based graph classification algorithms relies ...
In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified usi...
In this paper we present a fully automated approach to the segmentation of pediatric brain tumors in multi-spectral 3-D magnetic resonance images. It is a top-down segmentation app...
Michael Wels, Gustavo Carneiro, Alexander Aplas,...