Generalized Manifold-Ranking-Based Image Retrieval

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 , our method could work well whether or not the query image is in the databas...

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Bibliographic Details
Published in:IEEE transactions on image processing 2006-10, Vol.15 (10), p.3170-3177
Main Authors: JINGRUI HE, MINGJING LI, ZHANG, Hong-Jiang, HANGHANG TONG, CHANGSHUI ZHANG
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Content based retrieval
Database Management Systems
Databases, Factual
Exact sciences and technology
Feedback
History
Image converters
Image databases
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image retrieval
Information retrieval
Information Storage and Retrieval - methods
Information theory
Information, signal and communications theory
Learning
Learning systems
manifold ranking
Mathematical analysis
outside the database
Pattern Recognition, Automated - methods
Query processing
relevance feedback
Retrieval
Signal processing
Spreads
Telecommunications and information theory
User-Computer Interface
Vectors (mathematics)
Web sites
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Summary: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 , 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
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2006.877491