research-article

Tag ranking by propagating relevance over tag and image graphs

Authors:

Chunxia ZhaoAuthors Info & Claims

ICIMCS '12: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service

Pages 153 - 156

https://doi.org/10.1145/2382336.2382380

Published: 09 September 2012 Publication History

Abstract

In this paper, we explore the problem of tag ranking by propagating relevance over community-contributed images and their associated tags. To rank the tags more accurately, we propose a novel tag ranking scheme through a two-stage graph-based relevance propagation approach. The first stage constructs a tag graph on each image and implements a random walk process on it in order to get the initial relevance of each tag for one image and the second stage builds a kNN-sparse image graph and propagates the relevance of tags among the web images. The proposed approach is purely data-driven, since the explicit relevance models between tags and images are not assumed. More importantly, compared to existing tag ranking approaches, we propose to leverage the relevance propagation over two graphs, which take into count not only the relationship among tags but also the relationship among images. Extensive experiments have conducted on the NUS-WIDE dataset have demonstrated the effectiveness of the proposed approach.

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Cited By

FUJII TYOSHIDA SMUNEYASU M(2019)Video Search Reranking with Relevance Feedback Using Visual and Textual SimilaritiesIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E102.A.1900E102.A:12(1900-1909)Online publication date: 1-Dec-2019
https://doi.org/10.1587/transfun.E102.A.1900
Maenishi TTajima KAtzenbeck CRubart JMillard D(2019)Identifying Tags Describing Image ContentsProceedings of the 30th ACM Conference on Hypertext and Social Media10.1145/3342220.3344936(297-298)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3342220.3344936
Tang JLi MLi ZZhao C(2018)Tag ranking based on salient region graph propagationMultimedia Systems10.1007/s00530-014-0357-121:3(267-275)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1007/s00530-014-0357-1
Show More Cited By

Index Terms

Tag ranking by propagating relevance over tag and image graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding
2. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

cover image ACM Other conferences

ICIMCS '12: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service

September 2012

243 pages

ISBN:9781450316002

DOI:10.1145/2382336

General Chairs:
Xinguo Yu
Institute for Infocomm Research, Singapore
,
Rainer Lienhart
University of Augsburg, Germany
,
Program Chairs:
Zheng-Jun Zha
National University of Singapore
,
Yan Liu
Hong Kong Polytechnic University
,
Shin'ichi Satoh
National Institute of Informatics, Japan

Copyright © 2012 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]

Sponsors

National Science Foundation of China
CCNU: Central China Normal University
Daqian Vision: Daqian Vision
Microsoft Research: Microsoft Research
Beijing ACM SIGMM Chapter
NEC: NEC Labs China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 September 2012

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ICIMCS '12

Sponsor:

CCNU
Daqian Vision
Microsoft Research
NEC

ICIMCS '12: The 4th International Conference on Internet Multimedia Computing and Service

September 9 - 11, 2012

Wuhan, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

FUJII TYOSHIDA SMUNEYASU M(2019)Video Search Reranking with Relevance Feedback Using Visual and Textual SimilaritiesIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.E102.A.1900E102.A:12(1900-1909)Online publication date: 1-Dec-2019
https://doi.org/10.1587/transfun.E102.A.1900
Maenishi TTajima KAtzenbeck CRubart JMillard D(2019)Identifying Tags Describing Image ContentsProceedings of the 30th ACM Conference on Hypertext and Social Media10.1145/3342220.3344936(297-298)Online publication date: 12-Sep-2019
https://dl.acm.org/doi/10.1145/3342220.3344936
Tang JLi MLi ZZhao C(2018)Tag ranking based on salient region graph propagationMultimedia Systems10.1007/s00530-014-0357-121:3(267-275)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1007/s00530-014-0357-1
Li SPurushotham SChen CRen YKuo C(2017)Measuring and Predicting Tag Importance for Image RetrievalIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2017.265181839:12(2423-2436)Online publication date: 1-Dec-2017
https://doi.org/10.1109/TPAMI.2017.2651818
Songhe Feng Zheyun Feng Rong Jin (2015)Learning to Rank Image Tags With Limited Training ExamplesIEEE Transactions on Image Processing10.1109/TIP.2015.239581624:4(1223-1234)Online publication date: Apr-2015
https://doi.org/10.1109/TIP.2015.2395816
Torres SHiemstra DWeber ISerdyukov P(2014)Query recommendation in the information domain of childrenJournal of the Association for Information Science and Technology10.1002/asi.2305565:7(1368-1384)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1002/asi.23055

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