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Leveraging user comments for aesthetic aware image search reranking

Authors:

Jose San Pedro,

Nuria OliverAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 439 - 448

https://doi.org/10.1145/2187836.2187896

Published: 16 April 2012 Publication History

Abstract

The increasing number of images available online has created a growing need for efficient ways to search for relevant content. Text-based query search is the most common approach to retrieve images from the Web. In this approach, the similarity between the input query and the metadata of images is used to find relevant information. However, as the amount of available images grows, the number of relevant images also increases, all of them sharing very similar metadata but differing in other visual characteristics. This paper studies the influence of visual aesthetic quality in search results as a complementary attribute to relevance. By considering aesthetics, a new ranking parameter is introduced aimed at improving the quality at the top ranks when large amounts of relevant results exist. Two strategies for aesthetic rating inference are proposed: one based on visual content, another based on the analysis of user comments to detect opinions about the quality of images. The results of a user study with $58$ participants show that the comment-based aesthetic predictor outperforms the visual content-based strategy, and reveals that aesthetic-aware rankings are preferred by users searching for photographs on the Web.

References

[1]

R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying Aesthetics in Photographic Images Using a Computational Approach. In ECCV'06, volume 3953 of L.N. in Computer Science, pages 288--301. Springer.

Digital Library

[2]

S. Hasler and S. Susstrunk. Measuring colorfulness in real images. volume 5007, pages 87--95, 2003.

[3]

W. H. Hsu, L. S. Kennedy, and S. F. Chang. Video search reranking via information bottleneck principle. In ACM Multimedia '06, pages 35--44, NY, USA, 2006.

Digital Library

[4]

K. Q. Huang, Q. Wang, and Z. Y. Wu. Natural color image enhancement and evaluation algorithm based on human visual system. Comput. Vis. Image Underst., 103(1):52--63, 2006.

Digital Library

[5]

V. Jain and M. Varma. Learning to re-rank: query-dependent image re-ranking using click data. In Proc. ACM Conf. on World wide web, WWW '11, pages 277--286, NY, USA, 2011. ACM.

Digital Library

[6]

W. Jin, H. H. Ho, and R. K. Srihari. OpinionMiner: a novel machine learning system for web opinion mining and extraction. In Proc. ACM SIGKDD, KDD '09, pages 1195--1204, NY, USA, 2009. ACM.

Digital Library

[7]

Y. Jing and S. Baluja. Pagerank for product image search. In Proc. ACM Conf. on World Wide Web, WWW '08, pages 307--316, NY, USA, 2008. ACM.

Digital Library

[8]

Y. Ke, X. Tang, and F. Jing. The Design of High-Level Features for Photo Quality Assessment. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 1:419--426, June 2006.

Digital Library

[9]

C. W. K. Leung, S. C. F. Chan, F. L. Chung, and G. Ngai. A probabilistic rating inference framework for mining user preferences from reviews. World Wide Web, 14(2):187--215, Mar. 2011.

Digital Library

[10]

Y. Luo and X. Tang. Photo and Video Quality Evaluation: Focusing on the Subject. In ECCV '08, pages 386--399, Berlin, Heidelberg, 2008. Springer-Verlag.

Digital Library

[11]

P. Obrador, X. Anguera, R. de Oliveira, and N. Oliver. The role of tags and image aesthetics in social image search. In WSM '09, pages 65--72, NY, USA, 2009. ACM.

Digital Library

[12]

P. Obrador, L. Schmidt-Hackenberg, and N. Oliver. The role of image composition in image aesthetics. In IEEE ICIP 2010, pages 3185--3188, 2010.

[13]

R. Orendovici and J. Z. Wang. Training data collection system for a learning-based photographic aesthetic quality inference engine. In ACM Multimedia'10, pages 1575--1578, NY, USA, 2010.

Digital Library

[14]

B. Pang and L. Lee. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL '05, pages 115--124, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.

Digital Library

[15]

G. Peters. Aesthetic Primitives of Images for Visualization. In IEEE Int. Conf. Information Visualization, 2007, pages 316--325, July 2007.

Digital Library

[16]

J. San Pedro and S. Siersdorfer. Ranking and classifying attractiveness of photos in folksonomies. In Proc. ACM conf. on World wide web, WWW '09, pages 771--780, NY, USA, 2009.

Digital Library

[17]

N. Sawant, J. Li, and J. Z. Wang. Automatic image semantic interpretation using social action and tagging data. Multimedia Tools Appl., 51(1):213--246, 2011.

Digital Library

[18]

A. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199--222, Aug. 2004.

Digital Library

[19]

R. van Zwol, A. Rae, and L. G. Pueyo. Prediction of favourite photos using social, visual, and textual signals. In ACM Multimedia'10, pages 1015--1018, NY, USA, 2010.

Digital Library

[20]

M. Wang, B. Liu, and X. S. Hua. Accessible image search. In ACM Multimedia'09, pages 291--300, NY, USA, 2009.

Digital Library

[21]

L. Yang and A. Hanjalic. Supervised reranking for web image search. In ACM Multimedia'10, pages 183--192, NY, USA, 2010.

Digital Library

[22]

C. H. Yeh, Y. C. Ho, B. A. Barsky, and M. Ouhyoung. Personalized photograph ranking and selection system. In ACM Multimedia'10, pages 211--220, NY, USA, 2010.

Digital Library

[23]

J. Yu, Z.-J. Zha, M. Wang, and T.-S. Chua. Aspect ranking : Identifying important product aspects from online consumer reviews. Computational Linguistics, pages 1496--1505, 2011.

Digital Library

[24]

Z. J. Zha, L. Yang, T. Mei, M. Wang, Z. Wang, T. S. Chua, and X. S. Hua. Visual query suggestion: Towards capturing user intent in internet image search. ACM Trans. Multimedia Comput. Commun. Appl., 6, Aug. 2010.

Digital Library

Cited By

Maître H(2023)ReferencesAesthetics in Digital Photography10.1002/9781394225972.refs(271-294)Online publication date: 14-Jul-2023
https://doi.org/10.1002/9781394225972.refs
Ghosal KSmolic A(2022)Image Aesthetics Assessment Using Graph Attention Network2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956162(3160-3167)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956162
Khan S(2022)Business Intelligence Aspect for Emotions and Sentiments Analysis2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)10.1109/ICEEICT53079.2022.9768485(1-5)Online publication date: 16-Feb-2022
https://doi.org/10.1109/ICEEICT53079.2022.9768485
Show More Cited By

Index Terms

Leveraging user comments for aesthetic aware image search reranking
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Multimedia information systems

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Information

Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

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

Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Maître H(2023)ReferencesAesthetics in Digital Photography10.1002/9781394225972.refs(271-294)Online publication date: 14-Jul-2023
https://doi.org/10.1002/9781394225972.refs
Ghosal KSmolic A(2022)Image Aesthetics Assessment Using Graph Attention Network2022 26th International Conference on Pattern Recognition (ICPR)10.1109/ICPR56361.2022.9956162(3160-3167)Online publication date: 21-Aug-2022
https://doi.org/10.1109/ICPR56361.2022.9956162
Khan S(2022)Business Intelligence Aspect for Emotions and Sentiments Analysis2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)10.1109/ICEEICT53079.2022.9768485(1-5)Online publication date: 16-Feb-2022
https://doi.org/10.1109/ICEEICT53079.2022.9768485
Vanthana VKartheeban K(2022)Estimation of Accuracy Level for Sentiment Analysis using Machine Learning and Deep Learning Models2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)10.1109/ICACRS55517.2022.10029086(717-722)Online publication date: 13-Dec-2022
https://doi.org/10.1109/ICACRS55517.2022.10029086
Valenzise GKang CDufaux F(2021)Advances and Challenges in Computational Image AestheticsHuman Perception of Visual Information10.1007/978-3-030-81465-6_6(133-181)Online publication date: 22-Jul-2021
https://doi.org/10.1007/978-3-030-81465-6_6
Zeng HCao ZZhang LBovik A(2020)A Unified Probabilistic Formulation of Image Aesthetic AssessmentIEEE Transactions on Image Processing10.1109/TIP.2019.294177829(1548-1561)Online publication date: 2020
https://doi.org/10.1109/TIP.2019.2941778
Ghosal KRana ASmolic A(2019)Aesthetic Image Captioning From Weakly-Labelled Photographs2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)10.1109/ICCVW.2019.00556(4550-4560)Online publication date: Oct-2019
https://doi.org/10.1109/ICCVW.2019.00556
ROY HYamasaki THashimoto TChu WTsumura NYamasaki TShiratori TMotomura H(2018)Predicting Image Aesthetics using Objects in the SceneProceedings of the 2018 International Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia10.1145/3209693.3209698(14-19)Online publication date: 6-Jun-2018
https://dl.acm.org/doi/10.1145/3209693.3209698
Meguebli YKacimi MDoan BPopineau F(2017)Towards better news article recommendationWorld Wide Web10.1007/s11280-017-0436-220:6(1293-1312)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s11280-017-0436-2
Yang XMei TZhang YLiu JSatoh S(2016)Web Image Search Re-Ranking With Click-Based Similarity and TypicalityIEEE Transactions on Image Processing10.1109/TIP.2016.259365325:10(4617-4630)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1109/TIP.2016.2593653
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