Article

MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset

Authors:

Hao Li,

Meng Wang,

Xian-Sheng HuaAuthors Info & Claims

ICDMW '09: Proceedings of the 2009 IEEE International Conference on Data Mining Workshops

Pages 164 - 169

https://doi.org/10.1109/ICDMW.2009.46

Published: 06 December 2009 Publication History

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Abstract

In this paper, we introduce the second version of Microsoft Research Asia Multimedia (MSRA-MM), a dataset that aims to facilitate research in multimedia information retrieval and related areas. The images and videos in the dataset are collected from a commercial search engine with more than 1000 queries. It contains about 1 million images and 20,000 videos. We also provide the surrounding texts that are obtained from more than 1 million web pages. The images and videos have been comprehensively annotated, including their relevance levels to corresponding queries, semantic concepts of images, and category and quality information of videos. We define six standard tasks on the dataset: (1) image search reranking; (2) image annotation; (3) query-by-example image search; (4) video search reranking; (5) video categorization; and (6) video quality assessment.

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Chen WWang HZhang YDeng PLuo ZLi T(2024)T-Distributed Stochastic Neighbor Embedding for Co-Representation LearningACM Transactions on Intelligent Systems and Technology10.1145/362782315:2(1-18)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3627823
Shi CGu ZDuan CTian Q(2020)Multi-view adaptive semi-supervised feature selection with the self-paced learningSignal Processing10.1016/j.sigpro.2019.107332168:COnline publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.sigpro.2019.107332
Feng YXiao JZha ZZhang HYang Y(2018)Active learning for social image retrieval using Locally Regressive Optimal DesignNeurocomputing10.1016/j.neucom.2011.06.03795(54-59)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2011.06.037
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Index Terms

MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. Information systems applications
    1. Multimedia information systems

Index terms have been assigned to the content through auto-classification.

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In this paper, we proposed a framework to evaluate information retrieval systems in presence of multidimensional relevance. This is an important problem in tasks such as consumer health search, where the understandability and trustworthiness of ...

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ICDMW '09: Proceedings of the 2009 IEEE International Conference on Data Mining Workshops

December 2009

679 pages

ISBN:9780769539027

Publisher

IEEE Computer Society

United States

Publication History

Published: 06 December 2009

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Chen WWang HZhang YDeng PLuo ZLi T(2024)T-Distributed Stochastic Neighbor Embedding for Co-Representation LearningACM Transactions on Intelligent Systems and Technology10.1145/362782315:2(1-18)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1145/3627823
Shi CGu ZDuan CTian Q(2020)Multi-view adaptive semi-supervised feature selection with the self-paced learningSignal Processing10.1016/j.sigpro.2019.107332168:COnline publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.sigpro.2019.107332
Feng YXiao JZha ZZhang HYang Y(2018)Active learning for social image retrieval using Locally Regressive Optimal DesignNeurocomputing10.1016/j.neucom.2011.06.03795(54-59)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1016/j.neucom.2011.06.037
Yang ZWang HHan YZhu X(2018)Discriminative multi-task multi-view feature selection and fusion for multimedia analysisMultimedia Tools and Applications10.1007/s11042-017-5165-077:3(3431-3453)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-017-5165-0
Wang XChen RYan FZeng ZHong C(2018)Semi-supervised adaptive feature analysis and its application for multimedia understandingMultimedia Tools and Applications10.1007/s11042-017-4990-577:3(3083-3104)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s11042-017-4990-5
Song XZhang JHan YJiang J(2016)Semi-supervised feature selection via hierarchical regression for web image classificationMultimedia Systems10.1007/s00530-014-0390-022:1(41-49)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1007/s00530-014-0390-0
Hu JZhan DWu XJiang YZhou Z(2015)Pairwised Specific Distance Learning from Physical LinkagesACM Transactions on Knowledge Discovery from Data10.1145/27004059:3(1-27)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1145/2700405
Ji ZPang YHe YZhang H(2015)Semi-supervised LPP algorithms for learning-to-rank-based visual search rerankingInformation Sciences: an International Journal10.1016/j.ins.2014.10.037302:C(83-93)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.10.037
Hong ZPing W(2015)Image Classification with Local Linear Decoding and Global Multi-feature FusionProceedings, Part II, of the 16th Pacific-Rim Conference on Advances in Multimedia Information Processing -- PCM 2015 - Volume 931510.1007/978-3-319-24078-7_44(437-446)Online publication date: 16-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-24078-7_44
Luo PPeng JGuan ZFan J(2015)Multi-view semantic learning for data representationProceedings of the 2015th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I10.1007/978-3-319-23528-8_23(367-382)Online publication date: 7-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-23528-8_23
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