Cluster-based active learning for compact image classification
Détails
ID Serval
serval:BIB_F9E99BC7AA9C
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Cluster-based active learning for compact image classification
Titre de la conférence
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Honolulu, United States of America
Editeur
IEEE Conference Publications
ISBN
978-1-4244-9564-1
ISSN-L
2153-6996
Statut éditorial
Publié
Date de publication
2010
Pages
2824-2827
Langue
anglais
Résumé
In this paper, we consider active sampling to label pixels grouped
with hierarchical clustering. The objective of the method is to match
the data relationships discovered by the clustering algorithm with
the user's desired class semantics. The first is represented as a
complete tree to be pruned and the second is iteratively provided
by the user. The active learning algorithm proposed searches the
pruning of the tree that best matches the labels of the sampled points.
By choosing the part of the tree to sample from according to current
pruning's uncertainty, sampling is focused on most uncertain clusters.
This way, large clusters for which the class membership is already
fixed are no longer queried and sampling is focused on division of
clusters showing mixed labels. The model is tested on a VHR image
in a multiclass classification setting. The method clearly outperforms
random sampling in a transductive setting, but cannot generalize
to unseen data, since it aims at optimizing the classification of
a given cluster structure.
with hierarchical clustering. The objective of the method is to match
the data relationships discovered by the clustering algorithm with
the user's desired class semantics. The first is represented as a
complete tree to be pruned and the second is iteratively provided
by the user. The active learning algorithm proposed searches the
pruning of the tree that best matches the labels of the sampled points.
By choosing the part of the tree to sample from according to current
pruning's uncertainty, sampling is focused on most uncertain clusters.
This way, large clusters for which the class membership is already
fixed are no longer queried and sampling is focused on division of
clusters showing mixed labels. The model is tested on a VHR image
in a multiclass classification setting. The method clearly outperforms
random sampling in a transductive setting, but cannot generalize
to unseen data, since it aims at optimizing the classification of
a given cluster structure.
Création de la notice
25/11/2013 17:18
Dernière modification de la notice
20/08/2019 16:25
Données d'usage
