Article

Manifold-ranking based image retrieval

Authors:

Hong-Jiang Zhang,

Changshui ZhangAuthors Info & Claims

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

Pages 9 - 16

https://doi.org/10.1145/1027527.1027531

Published: 10 October 2004 Publication History

Abstract

In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance. In relevance feedback, if only positive examples are available, they are added to the query set to improve the retrieval result; if examples of both labels can be obtained, MRBIR discriminately spreads the ranking scores of positive and negative examples, considering the asymmetry between these two types of images. Furthermore, three active learning methods are incorporated into MRBIR, which select images in each round of relevance feedback according to different principles, aiming to maximally improve the ranking result. Experimental results on a general-purpose image database show that MRBIR attains a significant improvement over existing systems from all aspects.

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Index Terms

Manifold-ranking based image retrieval
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Information retrieval query processing
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Conferences

MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

October 2004

1028 pages

ISBN:1581138938

DOI:10.1145/1027527

General Chairs:
Henning Schulzrinne
Columbia University
,
Nevenka Dimitrova
Philips Research
,
Program Chairs:
Angela Sasse
UCL
,
Sue Moon
KAIST
,
Rainer Lienhart
U Augsburg

Copyright © 2004 ACM.

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Published: 10 October 2004

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MM04: 2004 12th Annual ACM International Conference on Multimedia

October 10 - 16, 2004

NY, New York, USA

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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https://dl.acm.org/doi/10.1109/TMM.2023.3294800
Wang SWang KLi X(2024)Diffusion Ranking Scores Based Improved Hypergraph Generation Method for Visual Classification Tasks2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS61622.2024.10606828(630-636)Online publication date: 17-May-2024
https://doi.org/10.1109/DDCLS61622.2024.10606828
Liu JZhao MZhan C(2024)Deep Representation-Based Fuzzy Graph Model for Content-Based Image RetrievalInternational Journal of Fuzzy Systems10.1007/s40815-024-01682-726:6(2011-2022)Online publication date: 19-Mar-2024
https://doi.org/10.1007/s40815-024-01682-7
Lin DWei VWong R(2023)Effective and Scalable Manifold Ranking-Based Image Retrieval with Output BoundACM Transactions on Knowledge Discovery from Data10.1145/356557417:5(1-31)Online publication date: 7-Apr-2023
https://dl.acm.org/doi/10.1145/3565574
Ammar KInoubli WZghal SBorji ANguifo E(2023)Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs.Procedia Computer Science10.1016/j.procs.2023.10.098225(1104-1113)Online publication date: 2023
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Li PGong SWang CFu Y(2022)Ranking Distance Calibration for Cross-Domain Few-Shot Learning2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52688.2022.00889(9089-9098)Online publication date: Jun-2022
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Furuya TOhbuchi R(2022)DeepDiffusion: Unsupervised Learning of Retrieval-Adapted Representations via Diffusion-Based Ranking on Latent Feature ManifoldIEEE Access10.1109/ACCESS.2022.321890910(116287-116301)Online publication date: 2022
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