Supervised learning of graph structure

Andrea Torsello*, Luca Rossi

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition.

Original languageEnglish
Title of host publicationSimilarity-Based Pattern Recognition
Subtitle of host publicationfirst international workshop, SIMBAD 2011, Venice, Italy, September 28-30, 2011. Proceedings
EditorsMarcello Pelillo, Edwin R. Hancock
Place of PublicationBerlin (DE)
Number of pages16
ISBN (Electronic)978-3-642-24471-1
ISBN (Print)978-3-642-24470-4
Publication statusPublished - 2011
Event1st international workshop on Similarity-Based Pattern Recognition - Venice, Italy
Duration: 28 Sept 201130 Sept 2011

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Workshop1st international workshop on Similarity-Based Pattern Recognition
Abbreviated titleSIMBAD 2011


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