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Communication dans un congrès
Manifold learning using robust graph Laplacian for interactive image search
Abstract : Interactive image search or relevance feedback is the process which helps a user refining his query and finding difficult target categories. This consists in partially labeling 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 feed- back 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 three-folds: it allows us (i) to integrate all the unlabeled images in the decision process (ii) to robustly capture the topology of the image set and (iii) to perform the search process inside the manifold. 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.
Littérature citée [32 références]
https://hal-enpc.archives-ouvertes.fr/hal-00834992
Contributeur : Pascal Monasse <>
Soumis le : mardi 18 juin 2013 - 14:39:23
Dernière modification le : vendredi 31 juillet 2020 - 10:44:06
Archivage à long terme le : : jeudi 19 septembre 2013 - 04:08:13
Contributeur : Pascal Monasse <>
Soumis le : mardi 18 juin 2013 - 14:39:23
Dernière modification le : vendredi 31 juillet 2020 - 10:44:06
Archivage à long terme le : : jeudi 19 septembre 2013 - 04:08:13
Fichier
CVPR08b.pdf
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