tutorial

Image Tag Assignment, Refinement and Retrieval

Authors:

Alberto Del BimboAuthors Info & Claims

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

Pages 1325 - 1326

https://doi.org/10.1145/2733373.2807419

Published: 13 October 2015 Publication History

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Abstract

This tutorial focuses on challenges and solutions for content-based image annotation and retrieval in the context of online image sharing and tagging. We present a unified review on three closely linked problems, i.e., tag assignment, tag refinement, and tag-based image retrieval. We introduce a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. Moreover, we present an open-source testbed, with training sets of varying sizes and three test datasets, to evaluate methods of varied learning complexity. A selected set of eleven representative works have been implemented and evaluated. During the tutorial we provide a practice session for hands on experience with the methods, software and datasets. For repeatable experiments all data and code are online at http://www.micc.unifi.it/tagsurvey

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Cited By

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Tyagi SKumar VChakraborty SShukla P(2023)Image Retrieval Technique Using Visual Features in Content-Based Image Retrieval SystemMachine Intelligence and Data Science Applications10.1007/978-981-99-1620-7_7(79-89)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-981-99-1620-7_7
Li PXie HMin SZha ZZhang Y(2022)Online Residual Quantization Via Streaming Data Correlation PreservingIEEE Transactions on Multimedia10.1109/TMM.2021.306248024(981-994)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3062480
Zhou RXia DWan JZhang S(2020)An Intelligent Video Tag Recommendation Method for Improving Video Popularity in Mobile Computing EnvironmentIEEE Access10.1109/ACCESS.2019.29613928(6954-6967)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2019.2961392
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Index Terms

Image Tag Assignment, Refinement and Retrieval
1. Information systems
  1. Information retrieval
    1. Document representation

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Information & Contributors

Information

Published In

MM '15: Proceedings of the 23rd ACM international conference on Multimedia

October 2015

1402 pages

ISBN:9781450334594

DOI:10.1145/2733373

General Chairs:
Xiaofang Zhou
The University of Queensland, Australia
,
Alan F. Smeaton
Dublin City University, Ireland
,
Qi Tian
The University of Texas at San Antonio, USA
,
Program Chairs:
Dick C.A. Bulterman
FXPAL, USA
,
Heng Tao Shen
The University of Queensland, Australia
,
Ketan Mayer-Patel
The University of North Carolina, USA
,
Shuicheng Yan
National University of Singapore, Singapore

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 October 2015

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Tutorial

Funding Sources

NSFC
Telecom Italia PhD grant funds
the Dutch national program COMMIT
the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China
the AQUIS-CH project granted by the Tuscany Region (Italy)
STW STORY
the EC's FP7

Conference

MM '15

Sponsor:

SIGMM

MM '15: ACM Multimedia Conference

October 26 - 30, 2015

Brisbane, Australia

Acceptance Rates

MM '15 Paper Acceptance Rate 56 of 252 submissions, 22%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all

Tyagi SKumar VChakraborty SShukla P(2023)Image Retrieval Technique Using Visual Features in Content-Based Image Retrieval SystemMachine Intelligence and Data Science Applications10.1007/978-981-99-1620-7_7(79-89)Online publication date: 2-Sep-2023
https://doi.org/10.1007/978-981-99-1620-7_7
Li PXie HMin SZha ZZhang Y(2022)Online Residual Quantization Via Streaming Data Correlation PreservingIEEE Transactions on Multimedia10.1109/TMM.2021.306248024(981-994)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3062480
Zhou RXia DWan JZhang S(2020)An Intelligent Video Tag Recommendation Method for Improving Video Popularity in Mobile Computing EnvironmentIEEE Access10.1109/ACCESS.2019.29613928(6954-6967)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2019.2961392
Mahajan AChaudhary S(2020)Image Context Based Similarity Retrieval SystemNew Trends in Computational Vision and Bio-inspired Computing10.1007/978-3-030-41862-5_7(73-80)Online publication date: 2020
https://doi.org/10.1007/978-3-030-41862-5_7
Chaudhary CGoyal PPhoebe Chen Y(2017)Exploiting visual and textual neighborhood information to improve image-tag relevance2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8257972(566-575)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8257972
Srinivasan BSheikh NKumar RVerma SGanguly N(2017)Usage Based Tag Enhancement of ImagesAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-57454-7_22(278-290)Online publication date: 23-Apr-2017
https://doi.org/10.1007/978-3-319-57454-7_22
Izadinia HRussell BFarhadi AHoffman MHertzmann AFriedland GNgo CShamma D(2015)Deep Classifiers from Image Tags in the WildProceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions10.1145/2814815.2814821(13-18)Online publication date: 30-Oct-2015
https://dl.acm.org/doi/10.1145/2814815.2814821

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