Article

A nearest-neighbor approach to relevance feedback in content based image retrieval

Author:

Giorgio GiacintoAuthors Info & Claims

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

Pages 456 - 463

https://doi.org/10.1145/1282280.1282347

Published: 09 July 2007 Publication History

Abstract

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. The main difficulties in exploiting relevance information are i) the gap between user perception of similarity and the similarity computed in the feature space used for the representation of image content, and ii) the availability of few training data (users typically label a few dozen of images). At present, SVM are extensively used to learn from relevance feedback due to their capability of effectively tackling the above difficulties. However, the performances of SVM depend on the tuning of a number of parameters. In this paper a different approach based on the nearest neighbor paradigm is proposed. Each image is ranked according to a relevance score depending on nearest-neighbor distances. This approach is proposed both in low-level feature spaces, and in "dissimilarity spaces", where image are represented in terms of their dissimilarities from the set of relevant images. Reported results show that the proposed approach allows recalling a higher percentage of images with respect to SVM-based techniques.

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Cited By

Ferrada SBustos BHogan A(2024)Similarity joins and clustering for SPARQLSemantic Web10.3233/SW-24354015:5(1701-1732)Online publication date: 9-Oct-2024
https://doi.org/10.3233/SW-243540
Hamid NHong JPark J(2024)(Nearest) Neighbors You Can Rely On: Formally Verified k-d Tree Construction and Search in CoqProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635960(1684-1693)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635960
Feng YMaeda KOgawa THaseyama M(2023)Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image RetrievalApplied Sciences10.3390/app1310594813:10(5948)Online publication date: 11-May-2023
https://doi.org/10.3390/app13105948
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Index Terms

A nearest-neighbor approach to relevance feedback in content based image retrieval
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

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cover image ACM Conferences

CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval

July 2007

655 pages

ISBN:9781595937339

DOI:10.1145/1282280

General Chairs:
Nicu Sebe
Univ. of Amsterdam, The Netherlands
,
Marcel Worring
Univ. of Amsterdam, The Netherlands

Copyright © 2007 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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SIGIR: ACM Special Interest Group on Information Retrieval

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Published: 09 July 2007

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CIVR07: International Conference on Image and Video Retrieval 2007

July 9 - 11, 2007

Amsterdam, The Netherlands

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Ferrada SBustos BHogan A(2024)Similarity joins and clustering for SPARQLSemantic Web10.3233/SW-24354015:5(1701-1732)Online publication date: 9-Oct-2024
https://doi.org/10.3233/SW-243540
Hamid NHong JPark J(2024)(Nearest) Neighbors You Can Rely On: Formally Verified k-d Tree Construction and Search in CoqProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3635960(1684-1693)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3635960
Feng YMaeda KOgawa THaseyama M(2023)Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image RetrievalApplied Sciences10.3390/app1310594813:10(5948)Online publication date: 11-May-2023
https://doi.org/10.3390/app13105948
Yanagi RTogo ROgawa THaseyama M(2023)Recallable Question Answering-Based Re-Ranking Considering Semantic Region for Cross-Modal RetrievalIEEE Open Journal of Signal Processing10.1109/OJSP.2023.32382804(1-11)Online publication date: 2023
https://doi.org/10.1109/OJSP.2023.3238280
Liu JYinchai WWei FHan QTao YZhao LLi XSun H(2023)Secure Cloud-Aided Approximate Nearest Neighbor Search on High-Dimensional DataIEEE Access10.1109/ACCESS.2023.332145711(109027-109037)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3321457
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Delussu RPutzu LLedda EFumera G(2023)Human-in-the-Loop Person Re-Identification as a Defence Against Adversarial AttacksImage Analysis and Processing - ICIAP 2023 Workshops10.1007/978-3-031-51023-6_28(330-342)Online publication date: 11-Sep-2023
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Ukey NYang ZYang WLi BLi R(2023)kNN Join for Dynamic High-Dimensional Data: A Parallel ApproachDatabases Theory and Applications10.1007/978-3-031-47843-7_1(3-16)Online publication date: 7-Nov-2023
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Yanagi RTogo ROgawa THaseyama M(2022)Interactive Re-ranking via Object Entropy-Guided Question Answering for Cross-Modal Image RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/348504218:3(1-17)Online publication date: 4-Mar-2022
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Čech PLokoč JSilva Y(2022)Pivot-based approximate k-NN similarity joins for big high-dimensional dataInformation Systems10.1016/j.is.2019.06.00687:COnline publication date: 21-Apr-2022
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