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Image Retrieval by User-oriented Ranking

Authors:

Xueming Qian,

Dan Lu,

Xiaoxiao LiuAuthors Info & Claims

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

Pages 511 - 514

https://doi.org/10.1145/2671188.2749326

Published: 22 June 2015 Publication History

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Abstract

Tag-based image search is an important method to process images contributed by social users in social media sharing websites like Flickr. However, existing ranking methods for tag-based image search frequently return results that are irrelevant, low-diversity or time-consuming. In this paper, we propose a user-oriented image ranking system with the consideration of image relevance, diversity and computation complexity, aiming to automatically rank images according to their visual information, semantic information and social clues. When you input a query in the user-oriented image search engine, images tagged with query are obtained as the initial results. The initial results include images contributed by different social users. Usually each user contributes several images. First we sort these users by inter-user ranking. Users that have a higher contribution to the given query rank higher. Then we sequentially implement intra-user ranking on the ranked user's image set, and only the most relevant image in each user's image set is selected. These selected images compose the final retrieval results. Experimental results on Flickr dataset show that our user-oriented ranking method is effective and efficient.

References

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Cited By

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Qian XLi CLan KHou XLi ZHan J(2018)POI Summarization by Aesthetics Evaluation From Crowd Source Social MediaIEEE Transactions on Image Processing10.1109/TIP.2017.276945427:3(1178-1189)Online publication date: Mar-2018
https://doi.org/10.1109/TIP.2017.2769454
Qian XLu DWang YZhu LTang YWang M(2017)Image Re-Ranking Based on Topic DiversityIEEE Transactions on Image Processing10.1109/TIP.2017.269962326:8(3734-3747)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2699623
Spyrou EMylonas P(2016)A survey on Flickr multimedia research challengesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.01.00651:C(71-91)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.engappai.2016.01.006

Index Terms

Image Retrieval by User-oriented Ranking
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

Information

Published In

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

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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Short-paper

Funding Sources

NSFC
Program 973
Microsoft Research Asia
Fundamental Research Funds for Central Universities

Conference

ICMR '15

Sponsor:

SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

Acceptance Rates

ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

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Qian XLi CLan KHou XLi ZHan J(2018)POI Summarization by Aesthetics Evaluation From Crowd Source Social MediaIEEE Transactions on Image Processing10.1109/TIP.2017.276945427:3(1178-1189)Online publication date: Mar-2018
https://doi.org/10.1109/TIP.2017.2769454
Qian XLu DWang YZhu LTang YWang M(2017)Image Re-Ranking Based on Topic DiversityIEEE Transactions on Image Processing10.1109/TIP.2017.269962326:8(3734-3747)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1109/TIP.2017.2699623
Spyrou EMylonas P(2016)A survey on Flickr multimedia research challengesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.01.00651:C(71-91)Online publication date: 1-May-2016
https://dl.acm.org/doi/10.1016/j.engappai.2016.01.006

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Improving social tag-based image retrieval with CBIR technique

Social image retrieval based on topic diversity

Boost search relevance for tag-based social image retrieval