Graph laplacian for interactive image retrieval - École des Ponts ParisTech Accéder directement au contenu
Communication Dans Un Congrès Année : 2008

Graph laplacian for interactive image retrieval

Résumé

Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in a step-by-step labeling of a very small fraction of an image database and iteratively refining a decision rule using both the labeled and unlabeled data. Training of this decision rule is referred to as transductive learning. Our work is an original approach for relevance feedback based on Graph Laplacian. We introduce a new Graph Laplacian which makes it possible to robustly learn the embedding of the manifold enclosing the dataset via a diffusion map. Our approach is two-folds: it allows us (i) to integrate all the unlabeled images in the decision process and (ii) to robustly capture the topology of the image set. Relevance feedback experiments were conducted on simple databases including Olivetti and Swedish as well as challenging and large scale databases including Corel. Comparisons show clear and consistent gain of our graph Laplacian method with respect to state-of-the art relevance feedback approaches.
Fichier principal
Vignette du fichier
ICASSP08.pdf (224.91 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00835145 , version 1 (18-06-2013)

Identifiants

  • HAL Id : hal-00835145 , version 1

Citer

Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert, Renaud Keriven. Graph laplacian for interactive image retrieval. ICASSP, Mar 2008, Las Vegas, United States. pp.817-820. ⟨hal-00835145⟩
148 Consultations
178 Téléchargements

Partager

Gmail Facebook X LinkedIn More