Page de Frédéric SUARD

Aller au contenu | Aller au menu | Aller à la recherche

Tag - graph kernel

Fil des billets

mardi, septembre 11 2007

Mesure de similarité de graphes par noyau de sacs de chemins

Par Frédéric Suard le mardi, septembre 11 2007, 10:09 - Publications

21e colloque GRETSI sur le traitement du signal et des images 11-14 septembre 2007, Troyes, France www.gretsi2007.org

Abstract

A common approach for classifying shock graphs is to use a dissimilarity measure on graphs and a distance based classifier. In this paper, we propose the use of kernel functions for data mining problems on shock graphs. The first contribution of the paper is to extend the class of graph kernel by proposing kernels based on bag of paths. Then, we propose a methodology for using these kernels for shock graphs retrieval. Our experimental results show that our approach is very competitive compared to graph matching approaches and is rather robust.

une annexe

mercredi, avril 25 2007

Kernel on Bag of Paths For Measuring Similarity of Shapes

Par Frédéric Suard le mercredi, avril 25 2007, 10:00 - Publications

European Symposium on Artificial Neural Networks 25 - 27 April 2007, Bruges, Belgique http://www.dice.ucl.ac.be/esann/

Abstract

A common approach for classifying shock graphs is to use a dissimilarity measure on graphs and a distance based classifier. In this paper, we propose the use of kernel functions for data mining problems on shock graphs. The first contribution of the paper is to extend the class of graph kernel by proposing kernels based on bag of paths. Then, we propose a methodology for using these kernels for shock graphs retrieval. Our experimental results show that our approach is very competitive compared to graph matching approaches and is rather robust.

2 annexes

mardi, septembre 6 2005

Détection de piétons par stéréovision et noyaux de graphes

Par Frédéric Suard le mardi, septembre 6 2005, 11:16 - Publications

20e colloque GRETSI sur le traitement du signal et des images 6-9 septembre 2005, Louvain-La-Neuve, Belgique. http://www.gretsi2005.org

Abstract This article presents a novel method concerning pedestrian detection, thanks to graph kernels. Nowadays, the pedestrian detection is a hard task, due to the variability of its shape : size and posture. To address this problem, we choose to transform a pedestrian into a graph. The aim of this method consists of extracting a graph from each object (pedestrian or non-pedestrian), contained in a database. We compute the kernel with the inner product between graphs in order to apply a supervised classifier, here the SVMs (Support Vector Machine). We applied this method on a real images database in order to test its efficiency, particularly for scale invariance, and we obtained a good classification rate.

Résumé – Cet article présente une méthode concernant la reconnaissance de piétons à l’aide de graphes et de méthodes à noyaux. La détection du piéton est limitée à cause de la grande variabilité de la forme du piéton : taille, posture. Pour surmonter ce problème, nous avons choisi de le représenter à l’aide d’un graphe. Le but de la méthode est d’extraire le graphe de chaque objet (piétons ou non-piétons) présent dans une base d’images et de calculer un noyau à partir de ces graphes afin d’effectuer un apprentissage supervisé basé sur les SVMs (Séparateurs à Vaste Marge). L’application sur une base d’images réelles nous permet de démontrer l’efficacité de cette méthode, au niveau des invariances en échelle, avec un bon taux de reconnaissance.

@inproceedings{fsuardgretsi05,
author = {Frédéric Suard and Alain Rakotomamonjy and Abdelaziz Bensrhair},
title = {Détection de piétons par stéréovision et noyaux de graphes},
booktitle = {GRETSI05, Louvain-la-Neuve, Belgique},
year = {2005},
pages = {686-686},}

2 annexes

lundi, juin 6 2005

Pedestrian Detection using Stereo-vision and Graph Kernels

Par Frédéric Suard le lundi, juin 6 2005, 12:58 - Publications

Intelligent Vehicles Symposium 6-8 juin 2005, Las Vegas, Nevada, Etats-Unis. http://www.ieeeiv.org/

Abstract This paper presents a method for pedestrian detection with stereovision and graph comparison. Images are segmented thanks to the NCut method applied on a single image, and the disparity is computed from a pair of images. This segmentation enables us to keep only shapes of potential obstacles, by eliminating the background. The comparison between two graphs is accomplished with a inner product for graph, and then the recognition stage is performed learning is done among several pedestrian and non-pedestrian graphs with SVM method. The results that are depicted are preliminary results but they show that this approach is very promising since it clearly demonstrates that our graph representation is able to deal with the variability of pedestrian pose.

@inproceedings{fsuardiv05,
author = {Frédéric Suard and Vincent Guigue and Alain Rakotomamonjy and Abdelaziz Bensrhair},
 title = {Pedestrian Detection using Stereo-vision and Graph Kernels},
 booktitle = {Intelligent Vehicles Symposium, Las Vegas, Nevada},
 year = {2005},
 month = {June},
 pages = {267--272},}

2 annexes