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ICASSP
2008
IEEE

Graph laplacian for interactive image retrieval

10 years 9 months ago
Graph laplacian for interactive image retrieval
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 ...
Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICASSP
Authors Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert, Renaud Keriven
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