article

Content based image retrieval via a transductive model

Authors:

Baojie FangAuthors Info & Claims

Journal of Intelligent Information Systems, Volume 42, Issue 1

Pages 95 - 109

https://doi.org/10.1007/s10844-013-0257-4

Published: 01 February 2014 Publication History

Abstract

Content based image retrieval plays an important role in the management of a large image database. However, the results of state-of-the-art image retrieval approaches are not so satisfactory for the well-known gap between visual features and semantic concepts. Therefore, a novel transductive learning scheme named random walk with restart based method (RWRM) is proposed, consisting of three major components: pre-filtering processing, relevance score calculation, and candidate ranking refinement. Firstly, to deal with the problem of large computation cost involved in a large image database, a pre-filtering processing is utilized to filter out the most irrelevant images while keeping the most relevant images according to the results of a manifold ranking algorithm. Secondly, the relevance between a query image and the remaining images are obtained with respect to the probability density estimation. Finally, a transductive learning model, namely a random walk with restart model, is utilized to refine the ranking taking into account both the pairwise information of unlabeled images and the relevance scores between query image and unlabeled images. Experiments conducted on a typical Corel dataset demonstrate the effectiveness of the proposed scheme.

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Cited By

Chun JLee GShin KJung J(2024)Random walk with restart on hypergraphs: fast computation and an application to anomaly detectionData Mining and Knowledge Discovery10.1007/s10618-023-00995-938:3(1222-1257)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00995-9
S NGangodkar D(2021)A novel image retrieval technique based on semi supervised clusteringMultimedia Tools and Applications10.1007/s11042-021-11542-380:28-29(35741-35769)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11042-021-11542-3
Singh SBatra S(2020)An efficient bi-layer content based image retrieval systemMultimedia Tools and Applications10.1007/s11042-019-08401-779:25-26(17731-17759)Online publication date: 21-Feb-2020
https://dl.acm.org/doi/10.1007/s11042-019-08401-7
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Published In

cover image Journal of Intelligent Information Systems

Journal of Intelligent Information Systems Volume 42, Issue 1

February 2014

172 pages

ISSN:0925-9902

Issue’s Table of Contents

Copyright © Copyright © 2014 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2014

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Cited By

Chun JLee GShin KJung J(2024)Random walk with restart on hypergraphs: fast computation and an application to anomaly detectionData Mining and Knowledge Discovery10.1007/s10618-023-00995-938:3(1222-1257)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00995-9
S NGangodkar D(2021)A novel image retrieval technique based on semi supervised clusteringMultimedia Tools and Applications10.1007/s11042-021-11542-380:28-29(35741-35769)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11042-021-11542-3
Singh SBatra S(2020)An efficient bi-layer content based image retrieval systemMultimedia Tools and Applications10.1007/s11042-019-08401-779:25-26(17731-17759)Online publication date: 21-Feb-2020
https://dl.acm.org/doi/10.1007/s11042-019-08401-7
Jung JPark NLee SKang UChirkova RYang JSuciu D(2017)BePIProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3035950(789-804)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3035950

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