research-article

Unsupervised Distance Learning by Rank Correlation Measures for Image Retrieval

Authors:

César Yugo Okada,

Daniel Carlos Guimarães Pedronette,

Ricardo da S. TorresAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 331 - 338

https://doi.org/10.1145/2671188.2749335

Published: 22 June 2015 Publication History

Abstract

Ranking accurately collection images is the main objective of Content-based Image Retrieval (CBIR) systems. In fact, the set of images ranked at the first positions generally defines the effectiveness of provided search services, i.e., they are used for assessing automatically the quality of search systems as this set usually contains the collection images that are of interest. Recently, the use of ranking information (e.g., rank correlation) has been used in different research initiatives with the objective of improving the effectiveness of image retrieval tasks. This paper presents a broad rank correlation analysis for unsupervised distance learning on image retrieval tasks. Various well-known rank correlation measures are considered and two new measures are proposed. Several experiments were conducted considering various image datasets involving shape, color, and texture descriptors. Experimental results demonstrate that ranking information can be exploited for distance learning tasks successfully. Evaluated approaches yield better results in terms of effectiveness than various state-of-the-art algorithms.

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Cited By

Presotto JValem Lde Sá NPedronette DPapa J(2024)Weakly supervised classification through manifold learning and rank-based contextual measuresNeurocomputing10.1016/j.neucom.2024.127717589(127717)Online publication date: Jul-2024
https://doi.org/10.1016/j.neucom.2024.127717
Pereira-Ferrero VLewis TValem LFerrero LPedronette DLatecki L(2024)Unsupervised affinity learning based on manifold analysis for image retrieval: A surveyComputer Science Review10.1016/j.cosrev.2024.10065753(100657)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100657
Presotto JValem Lde Sa NPedronette DPapa J(2021)Weakly Supervised Learning through Rank-based Contextual Measures2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412596(5752-5759)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412596
Show More Cited By

Index Terms

Unsupervised Distance Learning by Rank Correlation Measures for Image Retrieval
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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CAPES
AMD
FAPESP
CNPq
Microsoft Research

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ICMR '15

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SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

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ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Presotto JValem Lde Sá NPedronette DPapa J(2024)Weakly supervised classification through manifold learning and rank-based contextual measuresNeurocomputing10.1016/j.neucom.2024.127717589(127717)Online publication date: Jul-2024
https://doi.org/10.1016/j.neucom.2024.127717
Pereira-Ferrero VLewis TValem LFerrero LPedronette DLatecki L(2024)Unsupervised affinity learning based on manifold analysis for image retrieval: A surveyComputer Science Review10.1016/j.cosrev.2024.10065753(100657)Online publication date: Aug-2024
https://doi.org/10.1016/j.cosrev.2024.100657
Presotto JValem Lde Sa NPedronette DPapa J(2021)Weakly Supervised Learning through Rank-based Contextual Measures2020 25th International Conference on Pattern Recognition (ICPR)10.1109/ICPR48806.2021.9412596(5752-5759)Online publication date: 10-Jan-2021
https://doi.org/10.1109/ICPR48806.2021.9412596
Ye FZhao XLuo WLi DMin W(2020)Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class SimilarityIEEE Access10.1109/ACCESS.2020.30043608(116824-116839)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3004360
Sharma SGupta VJuneja M(2019)A survey of image data indexing techniquesArtificial Intelligence Review10.1007/s10462-018-9673-852:2(1189-1266)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10462-018-9673-8
Valem LOliveira CPedronette DAlmeida J(2018)Unsupervised Similarity Learning through Rank Correlation and kNN SetsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/324105314:4(1-23)Online publication date: 23-Oct-2018
https://dl.acm.org/doi/10.1145/3241053
Lin JAdjeroh DJiang BJiang Y(2018)fastWKendallComputational Statistics10.1007/s00180-017-0775-633:4(1823-1845)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s00180-017-0775-6
Valem LGuimarães Pedronette D(2017)UDLFACM SIGMultimedia Records10.1145/3173058.31730609:2(1)Online publication date: 8-Dec-2017
https://dl.acm.org/doi/10.1145/3173058.3173060
Valem LPedronette D(2017)Selection and Combination of Unsupervised Learning Methods for Image RetrievalProceedings of the 15th International Workshop on Content-Based Multimedia Indexing10.1145/3095713.3095741(1-6)Online publication date: 19-Jun-2017
https://dl.acm.org/doi/10.1145/3095713.3095741
Valem LPedronette DIonescu BSebe NFeng JLarson MLienhart RSnoek C(2017)An Unsupervised Distance Learning Framework for Multimedia RetrievalProceedings of the 2017 ACM on International Conference on Multimedia Retrieval10.1145/3078971.3079017(107-111)Online publication date: 6-Jun-2017
https://dl.acm.org/doi/10.1145/3078971.3079017
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