Manifold learning using robust Graph Laplacian for interactive image search

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 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 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. Co...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Hichem Sahbi, Patrick Etyngier, Jean-Yves Audibert, Renaud Keriven
• Keywords: Computer Vision, CVPR 2008, Decision Rule, Graph Laplacian, Graph Laplacian Method, Relevance Feedback, Relevance Feedback Approaches