research-article

Visual diversification of image search results

Authors:

Reinier H. van Leuken,

Ximena Olivares,

Roelof van ZwolAuthors Info & Claims

WWW '09: Proceedings of the 18th international conference on World wide web

Pages 341 - 350

https://doi.org/10.1145/1526709.1526756

Published: 20 April 2009 Publication History

Abstract

Due to the reliance on the textual information associated with an image, image search engines on the Web lack the discriminative power to deliver visually diverse search results. The textual descriptions are key to retrieve relevant results for a given user query, but at the same time provide little information about the rich image content.

In this paper we investigate three methods for visual diversification of image search results. The methods deploy lightweight clustering techniques in combination with a dynamic weighting function of the visual features, to best capture the discriminative aspects of the resulting set of images that is retrieved. A representative image is selected from each cluster, which together form a diverse result set.

Based on a performance evaluation we find that the outcome of the methods closely resembles human perception of diversity, which was established in an extensive clustering experiment carried out by human assessors.

References

[1]

A. Bhattacharyya. On a measure of divergence between two statistical populations defined by probability distribution. Bul l. Calcutta Math. Soc., 35:99--109, 1493.

[2]

D. Cai, X. He, Z. Li, W.-Y. Ma, and J.-R. Wen. Hierarchical clustering of www image search results using visual, textual and link information. In MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia, pages 952--959, New York, NY, USA, 2004. ACM.

Digital Library

[3]

S. A. Chatzichristo's and Y. S. Boutalis. Cedd: Color and edge directivity descriptor: A compact descriptor for image indexing and retrieval. In A. Gasteratos, M. Vincze, and J. K. Tsotsos, editors, ICVS, volume 5008 of Lecture Notes in Computer Science, pages 312--322. Springer, 2008.

Digital Library

[4]

R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computer Surveys, 40(2):1--60, 2008.

Digital Library

[5]

E. Fowlkes and C. Mallows. A method for comparing two hierarchical clusterings. Journal of the American Statistical Association, 78(383):553--569, 1983.

[6]

J. Goldberger, S. Gordon, and H. Greenspan. Unsupervised image-set clustering using an information theoretic framework. IEEE Transactions on Image Processing, 15(2):449--458, 2006.

Digital Library

[7]

J. Huang, S. R. Kumar, and R. Zabih. An automatic hierarchical image classification scheme. In MULTIMEDIA '98: Proceedings of the sixth ACM international conference on Multimedia, pages 219--228, 1998.

Digital Library

[8]

Y. Liu, D. Zhang, G. Lu, and W.--Y. Ma. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1):262--282, 2007.

Digital Library

[9]

M. MeilØa. Comparing clusterings : an information based distance. Journal of Multivariate Analysis, 98(5):873--895, 2007.

Digital Library

[10]

P.-A. Moellic, J.-E. Haugeard, and G. Pitel. Image clustering based on a shared nearest neighbors approach for tagged collections. In CIVR '08: Proceedings of the 2008 international conference on Content-based image and video retrieval, pages 269--278, New York, NY, USA, 2008. ACM.

Digital Library

[11]

P. Salembier and T. Sikora. Introduction to MPEG-7: Multimedia Content Description Interface. John Wiley & Sons, Inc., New York, NY, USA, 2002.

Digital Library

[12]

A. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intel ligence, 22(12):1349--1380, Dec 2000.

Digital Library

[13]

K. Song, Y. Tian, W. Gao, and T. Huang. Diversifying the image retrieval results. In MULTIMEDIA '06: Proceedings of the 14th annual ACM international conference on Multimedia, pages 707--710, New York, NY, USA, 2006. ACM.

Digital Library

[14]

H. Tamura, S. Mori, and T. Yamawaki. Texture features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, 8(6), 1978.

[15]

R. van Zwol. Multimedia strategies for b3-sdr, based on principle component analysis. In Advances in XML Information Retrieval, Lecture Notes in Computer Science. Springer, 2006.

Digital Library

[16]

R. van Zwol, V. Murdock, L. Garcia, and G. Ramirez. Diversifying image search with user generated content. In Proceedings of the International ACM Conference on Multimedia Information Retrieval, 2008.

Digital Library

[17]

D. Wallace. Comment. Journal of the American Statistical Association, 78(383):569--576, 1983.

[18]

S. Wang, F. Jing, J. He, Q. Du, and L. Zhang. Igroup: presenting web image search results in semantic clusters. In CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 587--596, New York, NY, USA, 2007. ACM.

Digital Library

[19]

K. Weinberger, M. Slaney, and R. van Zwol. Resolving tag ambiguity. In Proceedings of the 16th International ACM Conference on Multimedia (MM 2008), Vancouver, Canada, November 2008.

Digital Library

[20]

P. Wilkins, P. Ferguson, and A. F. Smeaton. Using score distributions for query-time fusion in multimediaretrieval. In MIR '06: Proceedings of

Digital Library

[21]

the 8th ACM international workshop on Multimedia information retrieval, pages 51--60, New York, NY, USA, 2006. ACM.

[22]

S. A. Yahia, P. Bhat, J. Shanmugasundaram, U. Srivastava, and E. Vee. Efficient online computation of diverse query results. In Proceedings of VLDB, 2007.

[23]

B. Zhang, H. Li, Y. Liu, L. Ji, W. Xi, W. Fan, Z. Chen, and W.-Y. Ma. Improving web search results using affinity graph. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research

Digital Library

[24]

and development in information retrieval, pages 504--511, New York, NY, USA, 2005. ACM.

[25]

C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW '05: Proceedings of the 14th international conference on World Wide Web, pages 22--32, New York, NY, USA, 2005. ACM.

Digital Library

Cited By

Srinivasan HSchumann CSinha AMadras DOlanubi GBeutel ARicco SChen J(2024)Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People ImagesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658940(797-821)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658940
Li JMoskovitch YStoyanovich JJagadish H(2023)Query Refinement for Diversity Constraint SatisfactionProceedings of the VLDB Endowment10.14778/3626292.362629517:2(106-118)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.14778/3626292.3626295
Lei SGong YXiao XZhou YZhang J(2023)Boosting Diversity in Visual Search with Pareto Non-Dominated Re-RankingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362529620:3(1-23)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3625296
Show More Cited By

Index Terms

Visual diversification of image search results
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
2. Information systems
  1. Information retrieval
    1. Document representation
  2. World Wide Web

Recommendations

Interactive browsing via diversified visual summarization for image search results

Presenting and browsing image search results play key roles in helping users to find desired images from search results. Most existing commercial image search engines present them, dependent on a ranked list. However, such a scheme suffers from at least ...
Diversifying image search with user generated content
MIR '08: Proceedings of the 1st ACM international conference on Multimedia information retrieval

Large-scale image retrieval on the Web relies on the availability of short snippets of text associated with the image. This user-generated content is a primary source of information about the content and context of an image. While traditional ...
Hierarchical clustering of WWW image search results using visual, textual and link information
MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia

We consider the problem of clustering Web image search results. Generally, the image search results returned by an image search engine contain multiple topics. Organizing the results into different semantic clusters facilitates users' browsing. In this ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '09: Proceedings of the 18th international conference on World wide web

April 2009

1280 pages

ISBN:9781605584874

DOI:10.1145/1526709

General Chairs:
Juan Quemada
DIT-UPM
,
Gonzalo León
DIT-UPM
,
Program Chairs:
Yoelle Maarek
Google Inc., Israel
,
Wolfgang Nejdl
L3S and Hannover University

Copyright © 2009 IW3C2 org.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 April 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '09

Sponsor:

WWW '09: The 18th International World Wide Web Conference

April 20 - 24, 2009

Madrid, Spain

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

188
Total Citations
View Citations
1,101
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)2

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Srinivasan HSchumann CSinha AMadras DOlanubi GBeutel ARicco SChen J(2024)Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People ImagesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658940(797-821)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658940
Li JMoskovitch YStoyanovich JJagadish H(2023)Query Refinement for Diversity Constraint SatisfactionProceedings of the VLDB Endowment10.14778/3626292.362629517:2(106-118)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.14778/3626292.3626295
Lei SGong YXiao XZhou YZhang J(2023)Boosting Diversity in Visual Search with Pareto Non-Dominated Re-RankingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362529620:3(1-23)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3625296
Naik RNushi B(2023)Social Biases through the Text-to-Image Generation LensProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604711(786-808)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1145/3600211.3604711
Bhalgat YHenriques JZisserman A(2023)A Light Touch Approach to Teaching Transformers Multi-view Geometry2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00480(4958-4969)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00480
Du MRamisa AK C AChanda SWang MRajesh NLi SHu YZhou TLakshminarayana NTran SGray DZhang ARangwala H(2022)Amazon Shop the Look: A Visual Search System for Fashion and HomeProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539071(2822-2830)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539071
Zhu XLi ZWang XJiang XSun PWang XXiao YYuan N(2022)Multi-Modal Knowledge Graph Construction and Application: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3224228(1-20)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3224228
Tekli J(2022)An overview of cluster-based image search result organization: background, techniques, and ongoing challengesKnowledge and Information Systems10.1007/s10115-021-01650-9Online publication date: 11-Feb-2022
https://doi.org/10.1007/s10115-021-01650-9
Zhao KCai ZSui QWei EZhu K(2022)Clustering Image Search Results by Entity DisambiguationMachine Learning and Knowledge Discovery in Databases10.1007/978-3-662-44845-8_24(369-384)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-662-44845-8_24
Petridis SShin HChilton L(2021)SymbolFinder: Brainstorming Diverse Symbols Using Local Semantic NetworksThe 34th Annual ACM Symposium on User Interface Software and Technology10.1145/3472749.3474757(385-399)Online publication date: 10-Oct-2021
https://dl.acm.org/doi/10.1145/3472749.3474757
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents