Paper Info
Title
Generalized manifold-ranking-based image retrieval.
Abstract
In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR [12], our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques.
Year
DOI
Venue
2006
10.1109/TIP.2006.877491
IEEE Transactions on Image Processing
Keywords
Field
DocType
general transductive,manifold-ranking-based image retrieval,existing transductive,query image,general transductive learning framework,visual databases,generalized manifold-ranking-based image retrieval,active learning,image retrieval,outside the database,retrieval result,general-purpose image database,relevance feedback,manifold ranking,corel image,pseudo seed vector,generalized manifold-ranking-based image,unlabeled image,query concept,transductive learning
Transduction (machine learning),Data mining,Relevance feedback,Computer science,Image retrieval,Artificial intelligence,Manifold ranking,Computer vision,Active learning,Automatic image annotation,Query expansion,Pattern recognition,Visual Word
Journal
Volume
Issue
ISSN
15
10
1057-7149
Citations 
PageRank 
References 
68
2.39
28
Authors
5
Name
Order
Citations
PageRank
Jingrui He197775.40
Mingjing Li23076192.39
Hong-Jiang ZHANG3173781393.22
Hanghang Tong43560202.37
Changshui Zhang55506323.40