Generalized manifold-ranking-based image retrieval.

Full 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.

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Author information

Author/s: He, Jingrui (J); Li, Mingjing (M); Zhang, Hong-Jiang (HJ); Tong, Hanghang (H); Zhang, Changshui (C);

Affiliation: Automation Department, Tsinghua University, Beijing, China. hejingrui98(-atsign-)mails.tsinghua.edu.cn

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society (IEEE Trans Image Process), published in United States. (Language: eng)

Reference: 2006-Oct; vol 15 (issue 10) : pp 3170-7

Dates: Created 2006/10/06; Completed 2006/11/20;

PMID: 17022278, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Pattern Recognition, Automated - methods
User-Computer Interface

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