Supervised learning of graph structure

Andrea Torsello, Luca Rossi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

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 - First International Workshop, SIMBAD 2011, Proceedings
Pages117-132
Number of pages16
DOIs
Publication statusPublished - Sep 2011
Externally publishedYes
Event1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011 - Venice, Italy
Duration: 28 Sep 201130 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7005 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011
Country/TerritoryItaly
CityVenice
Period28/09/1130/09/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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