DocumentCode :
3407191
Title :
Graph laplacian for interactive image retrieval
Author :
Sahbi, Hichem ; Etyngier, Patrick ; Audibert, Jean-Yves ; Keriven, Renaud
Author_Institution :
CNRS, Telecom ParisTech, Paris
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
817
Lastpage :
820
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 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.
Keywords :
graph theory; image retrieval; learning (artificial intelligence); visual databases; decision rule; graph Laplacian; image database; image retrieval; state-of-the art relevance feedback approach; statistical learning; transductive learning; Displays; Feedback; Image databases; Image retrieval; Labeling; Laplace equations; Radio frequency; Robustness; Telecommunications; Topology; Graph Laplacian and Image retrieval; Statistical Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4517735
Filename :
4517735
Link To Document :
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