research-article

Effective, Efficient, and Scalable Unsupervised Distance Learning in Image Retrieval Tasks

Authors:

Lucas Pascotti Valem,

Daniel Carlos Guimarães Pedronette,

Ricardo da S. Torres,

Jurandy AlmeidaAuthors Info & Claims

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

Pages 51 - 58

https://doi.org/10.1145/2671188.2749336

Published: 22 June 2015 Publication History

Abstract

Various unsupervised learning methods have been proposed with significant improvements in the effectiveness of image search systems. However, despite the relevant effectiveness gains, these approaches commonly require high computation efforts, not addressing properly efficiency and scalability requirements. In this paper, we present a novel unsupervised learning approach for improving the effectiveness of image retrieval tasks. The proposed method is also scalable and efficient as it exploits parallel and heterogeneous computing on CPU and GPU devices. Extensive experiments were conducted considering five different public image collections and several descriptors. This rigorous experimental protocol evaluates the effectiveness, efficiency, and scalability of the proposed approach, and compares it with previous methods. Experimental results demonstrate that high effectiveness gains (up to +29%) can be obtained requiring small run times.

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Cited By

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
Leticio GValem LLopes LPedronette DEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)pyUDLF: A Python Framework for Unsupervised Distance Learning TasksProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613466(9680-9684)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613466
Pascotti Valem LPedronette DLatecki L(2023)Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold LearningIEEE Transactions on Image Processing10.1109/TIP.2023.326886832(2811-2826)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3268868
Show More Cited By

Index Terms

Effective, Efficient, and Scalable Unsupervised Distance Learning in Image Retrieval Tasks
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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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AMD
CNPq
FAPESP
CAPES
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

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
Leticio GValem LLopes LPedronette DEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)pyUDLF: A Python Framework for Unsupervised Distance Learning TasksProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613466(9680-9684)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613466
Pascotti Valem LPedronette DLatecki L(2023)Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold LearningIEEE Transactions on Image Processing10.1109/TIP.2023.326886832(2811-2826)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3268868
Pisani FPascotti Valem LGuimarães Pedronette Dda S. Torres RBorin EBreternitz M(2020)A unified model for accelerating unsupervised iterative re‐ranking algorithmsConcurrency and Computation: Practice and Experience10.1002/cpe.570232:14Online publication date: 3-Mar-2020
https://doi.org/10.1002/cpe.5702
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
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
Valem LGuimaraes Pedronette D(2016)Unsupervised Similarity Learning through Cartesian Product of Ranking References for Image Retrieval Tasks2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI.2016.042(249-256)Online publication date: Oct-2016
https://doi.org/10.1109/SIBGRAPI.2016.042

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