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Manifold Learning using Robust Graph Laplacian for Interactive Image Search Hichem Sahbi
 

Summary: Manifold Learning using Robust Graph Laplacian for Interactive Image Search
Hichem Sahbi
UMR 5141, CNRS
Telecom ParisTech, France
sahbi@telecom-paristech.fr
Patrick Etyngier, Jean-Yves Audibert and Renaud Keriven
Certis Lab
ENPC ParisTech, France
{etyngier,audibert,keriven}@certis.enpc.fr
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 unla-
beled 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 em-

  

Source: Audibert, Jean-Yves - Département d'Informatique, École Normale Supérieure
École Normale Supérieure, Laboratoire d'Informatique, WILLOW Computer vision and machine learning research laboratory

 

Collections: Computer Technologies and Information Sciences; Engineering