research-article

Fashion-focused creative commons social dataset

Authors:

Maria Menendez,

Mihai Georgescu,

Claudio Massari,

Ismail Sengor Altingovde,

Davide Martinenghi,

Mark Melenhorst,

Raynor Vliegendhart,

Martha LarsonAuthors Info & Claims

MMSys '13: Proceedings of the 4th ACM Multimedia Systems Conference

Pages 72 - 77

https://doi.org/10.1145/2483977.2483984

Published: 28 February 2013 Publication History

Abstract

In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on image's tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.

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Cited By

Martinenghi D(2025)Simplified Integrity Checking for an Expressive Class of Denial ConstraintsAlgorithms10.3390/a1803012318:3(123)Online publication date: 20-Feb-2025
https://doi.org/10.3390/a18030123
Islam SJoardar SSekh* A(2023)A Survey on Fashion Image RetrievalACM Computing Surveys10.1145/363655256:6(1-25)Online publication date: 13-Dec-2023
https://dl.acm.org/doi/10.1145/3636552
Islam SJoardar SSekh A(2023)Ornament image retrieval using few-shot learningInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00299-012:2Online publication date: 31-Aug-2023
https://doi.org/10.1007/s13735-023-00299-0
Show More Cited By

Index Terms

Fashion-focused creative commons social dataset
1. Applied computing
  1. Computers in other domains
    1. Digital libraries and archives
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Document collection models
  2. Information systems applications
    1. Digital libraries and archives

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Published In

cover image ACM Conferences

MMSys '13: Proceedings of the 4th ACM Multimedia Systems Conference

February 2013

304 pages

ISBN:9781450318945

DOI:10.1145/2483977

General Chair:
Carsten Griwodz
Simula Research Laboratory & University of Oslo, Norway

Copyright © 2013 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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Publication History

Published: 28 February 2013

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Research-article

Funding Sources

Seventh Framework Programme

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MMSys '13

Sponsor:

MMSys '13: Multimedia Systems Conference 2013

February 28 - March 1, 2013

Oslo, Norway

Acceptance Rates

MMSys '13 Paper Acceptance Rate 15 of 63 submissions, 24%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Citations

Cited By

Martinenghi D(2025)Simplified Integrity Checking for an Expressive Class of Denial ConstraintsAlgorithms10.3390/a1803012318:3(123)Online publication date: 20-Feb-2025
https://doi.org/10.3390/a18030123
Islam SJoardar SSekh* A(2023)A Survey on Fashion Image RetrievalACM Computing Surveys10.1145/363655256:6(1-25)Online publication date: 13-Dec-2023
https://dl.acm.org/doi/10.1145/3636552
Islam SJoardar SSekh A(2023)Ornament image retrieval using few-shot learningInternational Journal of Multimedia Information Retrieval10.1007/s13735-023-00299-012:2Online publication date: 31-Aug-2023
https://doi.org/10.1007/s13735-023-00299-0
Yu HZhang CLi JZhang S(2022)Robust Sparse Weighted Classification For CrowdsourcingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3201955(1-13)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3201955
Han JChen QJin XXu WYang WKumar SZhao LSundaram HKumar R(2021)FITNetProceedings of the ACM on Human-Computer Interaction10.1145/34492275:CSCW1(1-20)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449227
Ma QOlshevsky ALarochelle HRanzato MHadsell RBalcan MLin H(2020)Adversarial crowdsourcing through robust rank-one matrix completionProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3497556(21841-21852)Online publication date: 6-Dec-2020
https://dl.acm.org/doi/10.5555/3495724.3497556
Jin YCarman MZhu YXiang Y(2020)A technical survey on statistical modelling and design methods for crowdsourcing quality controlArtificial Intelligence10.1016/j.artint.2020.103351(103351)Online publication date: Jun-2020
https://doi.org/10.1016/j.artint.2020.103351
Robert L(2019)Crowdsourcing Controls: A Review and Research Agenda for Crowdsourcing Controls Used for Macro-tasksMacrotask Crowdsourcing10.1007/978-3-030-12334-5_3(45-126)Online publication date: 7-Aug-2019
https://doi.org/10.1007/978-3-030-12334-5_3
Zou XWong WMo D(2018)Fashion Meets AI TechnologyArtificial Intelligence on Fashion and Textiles10.1007/978-3-319-99695-0_31(255-267)Online publication date: 14-Oct-2018
https://doi.org/10.1007/978-3-319-99695-0_31
Catallo IMartinenghi D(2017)The Dimensions of Crowdsourcing Task DesignWeb Engineering10.1007/978-3-319-60131-1_25(394-402)Online publication date: 1-Jun-2017
https://doi.org/10.1007/978-3-319-60131-1_25
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