AustralieFrV1, Main, Merge, bibRecord, 006F05

Graph attribute embedding via Riemannian submersion learning

Identifieur interne : 006F05 ( Main/Merge ); précédent : 006F04; suivant : 006F06

Graph attribute embedding via Riemannian submersion learning

Auteurs : HAIFENG ZHAO [République populaire de Chine] ; Antonio Robles-Kelly [Australie] ; JUN ZHOU [Australie] ; JIANFENG LU [République populaire de Chine] ; Jing-Yu Yang [République populaire de Chine]

Source :

Computer vision and image understanding : (Print) [ 1077-3142 ] ; 2011.

RBID : Pascal:12-0017653

Descripteurs français

Pascal (Inist)
- Vision ordinateur, Concordance forme, Traitement image, Classification, Base de données, Espace métrique, Catégorisation, Estimation densité, Mesure densité, Intervalle confiance, Densité probabilité, Méthode itérative, Approximation L2, Sommet graphe, Probabilité a posteriori, Couplage graphe, Loi a posteriori, ., Reconnaissance objet.
Wicri :
- topic : Classification, Base de données.

English descriptors

KwdEn :
- Categorization, Classification, Computer vision, Confidence interval, Database, Density estimation, Density measurement, Graph matching, Image processing, Iterative method, L2 approximation, Metric space, Object recognition, Pattern matching, Posterior distribution, Posterior probability, Probability density, Vertex(graph).

Abstract

In this paper, we tackle the problem of embedding a set of relational structures into a metric space for purposes of matching and categorisation. To this end, we view the problem from a Riemannian perspective and make use of the concepts of charts on the manifold to define the embedding as a mixture of class-specific submersions. Formulated in this manner, the mixture weights are recovered using a probability density estimation on the embedded graph node coordinates. Further, we recover these class-specific submersions making use of an iterative trust-region method so as to minimise the L2 norm between the hard limit of the graph-vertex posterior probabilities and their estimated values. The method presented here is quite general in nature and allows tasks such as matching, categorisation and retrieval. We show results on graph matching, shape categorisation and digit classification on synthetic data, the MNIST dataset and the MPEG-7 database.

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Le document en format XML

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Graph attribute embedding via Riemannian submersion learning

Graph attribute embedding via Riemannian submersion learning

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri