research-article

Efficient manifold ranking for image retrieval

Authors:

Jiebo LuoAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 525 - 534

https://doi.org/10.1145/2009916.2009988

Published: 24 July 2011 Publication History

Abstract

Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, both in graph construction and ranking computation stages, which significantly limits its applicability to very large data sets. In this paper, we extend the original manifold ranking algorithm and propose a new framework named Efficient Manifold Ranking (EMR). We aim to address the shortcomings of MR from two perspectives: scalable graph construction and efficient computation. Specifically, we build an anchor graph on the data set instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The experimental results on a real world image database demonstrate the effectiveness and efficiency of our proposed method. With a comparable performance to the original manifold ranking, our method significantly reduces the computational time, makes it a promising method to large scale real world retrieval problems.

References

[1]

S. Agarwal. Ranking on graph data. In Proceedings of the 23rd International Conference on Machine Learning, pages 25--32, 2006.

Digital Library

[2]

S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer networks and ISDN systems, 30(1--7):107--117, 1998.

Digital Library

[3]

J. Bu, S. Tan, C. Chen, C. Wang, H. Wu, L. Zhang, and X. He. Music recommendation by unified hypergraph: combining social media information and music content. In Proceedings of the 18th annual ACM international conference on Multimedia, pages 391--400, 2010.

Digital Library

[4]

C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.

Digital Library

[5]

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

Digital Library

[6]

A. Frieze, R. Kannan, and S. Vempala. Fast Monte Carlo algorithms for finding low-rank approximations. Journal of the ACM (JACM), 51(6):1025--1041, 2004.

Digital Library

[7]

W. Gao, P. Cai, K. Wong, and A. Zhou. Learning to rank only using training data from related domain. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 162--169. ACM, 2010.

Digital Library

[8]

Z. Guan, J. Bu, Q. Mei, C. Chen, and C. Wang. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pages 540--547, 2009.

Digital Library

[9]

J. He, M. Li, H. Zhang, H. Tong, and C. Zhang. Manifold-ranking based image retrieval. In Proceedings of the 12th annual ACM international conference on Multimedia, pages 9--16, 2004.

Digital Library

[10]

J. He, M. Li, H. Zhang, H. Tong, and C. Zhang. Generalized manifold-ranking-based image retrieval. IEEE Transactions on Image Processing, 15(10):3170--3177, 2006.

Digital Library

[11]

R. He, Y. Zhu, and W. Zhan. Fast Manifold-Ranking for Content-Based Image Retrieval. In ISECS International Colloquium on Computing, Communication, Control, and Management.

[12]

X. He, D. Cai, and J. Han. Learning a maximum margin subspace for image retrieval. IEEE Transactions on Knowledge and Data Engineering, pages 189--201, 2007.

Digital Library

[13]

J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 119--126, 2003.

Digital Library

[14]

T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu. An efficient k-means clustering algorithm: Analysis and implementation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):881--892, 2002.

Digital Library

[15]

J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5):604--632, 1999.

Digital Library

[16]

B. Li, E. Chang, and C. Wu. DPF-a perceptual distance function for image retrieval. In International Conference on Image Processing, volume 2, pages 597--600, 2002.

[17]

D. Liu, X. Hua, L. Yang, M. Wang, and H. Zhang. Tag ranking. In Proceedings of the 18th international conference on World wide web, pages 351--360, 2009.

Digital Library

[18]

W. Liu, J. He, and S. Chang. Large graph construction for scalable semi-supervised learning. In Proceedings of the 27th International Conference on Machine Learning, pages 679--686, 2010.

Digital Library

[19]

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

Digital Library

[20]

D. Lowe. Object recognition from local scale-invariant features. In International Conference on Computer Vision, page 1150, 1999.

Digital Library

[21]

C. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, 2008.

Digital Library

[22]

J. Ponte and W. Croft. A language modeling approach to information retrieval. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 275--281. ACM, 1998.

Digital Library

[23]

S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323, 2000.

[24]

Y. Rui, T. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool for interactive content-based image retrieval. Circuits and Systems for Video Technology, IEEE Transactions on, 8(5):644--655, 2002.

Digital Library

[25]

S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proceedings of the 9th ACM international conference on Multimedia, pages 107--118, 2001.

Digital Library

[26]

M. Tsai, T. Liu, T. Qin, H. Chen, and W. Ma. FRank: a ranking method with fidelity loss. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 383--390. ACM, 2007.

Digital Library

[27]

R. Veltkamp and M. Tanase. Content-Based Image Retrieval Systems: A Survey. 2002.

[28]

X. Wan, J. Yang, and J. Xiao. Manifold-ranking based topic-focused multi-document summarization. In Proceedings of the 20th International Joint Conference on Artifical Intelligence, pages 2903--2908, 2007.

Digital Library

[29]

L. Wang, J. Lin, and D. Metzler. Learning to efficiently rank. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 138--145, 2010.

Digital Library

[30]

C. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In Advances in Neural Information Processing Systems 13, 2001.

Digital Library

[31]

H. Yu, M. Li, H. Zhang, and J. Feng. Color texture moments for content-based image retrieval. In International Conference on Image Processing, volume 3, pages 929--932, 2002.

[32]

K. Yu, J. Bi, and V. Tresp. Active learning via transductive experimental design. In Proceedings of the 23rd international conference on Machine learning, pages 1081--1088, 2006.

Digital Library

[33]

K. Yu, T. Zhang, and Y. Gong. Nonlinear learning using local coordinate coding. In Advances in Neural Information Processing Systems, 2009.

[34]

X. Yuan, X. Hua, M. Wang, and X. Wu. Manifold-ranking based video concept detection on large database and feature pool. In Proceedings of the 14th annual ACM International Conference on Multimedia, pages 623--626, 2006.

Digital Library

[35]

K. Zhang, J. Kwok, and B. Parvin. Prototype vector machine for large scale semi-supervised learning. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 1233--1240, 2009.

Digital Library

[36]

D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems, pages 595--602, 2004.

[37]

D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schölkopf. Ranking on data manifolds. In Advances in Neural Information Processing Systems, page 169, 2004.

Cited By

Liu JZhao MZhan C(2024)Deep Representation-Based Fuzzy Graph Model for Content-Based Image RetrievalInternational Journal of Fuzzy Systems10.1007/s40815-024-01682-726:6(2011-2022)Online publication date: 19-Mar-2024
https://doi.org/10.1007/s40815-024-01682-7
Lin DWei VWong R(2023)Effective and Scalable Manifold Ranking-Based Image Retrieval with Output BoundACM Transactions on Knowledge Discovery from Data10.1145/356557417:5(1-31)Online publication date: 7-Apr-2023
https://dl.acm.org/doi/10.1145/3565574
Minh PViet NSon NAnh BJaafar J(2023)RelaHash: Deep Hashing With Relative PositionIEEE Access10.1109/ACCESS.2023.325910411(30094-30108)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3259104
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Index Terms

Efficient manifold ranking for image retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. Information systems applications
    1. Multimedia information systems
      1. Multimedia databases

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Liu JZhao MZhan C(2024)Deep Representation-Based Fuzzy Graph Model for Content-Based Image RetrievalInternational Journal of Fuzzy Systems10.1007/s40815-024-01682-726:6(2011-2022)Online publication date: 19-Mar-2024
https://doi.org/10.1007/s40815-024-01682-7
Lin DWei VWong R(2023)Effective and Scalable Manifold Ranking-Based Image Retrieval with Output BoundACM Transactions on Knowledge Discovery from Data10.1145/356557417:5(1-31)Online publication date: 7-Apr-2023
https://dl.acm.org/doi/10.1145/3565574
Minh PViet NSon NAnh BJaafar J(2023)RelaHash: Deep Hashing With Relative PositionIEEE Access10.1109/ACCESS.2023.325910411(30094-30108)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3259104
Gupta ABegum S(2023)An Anchor-Based Fuzzy Rough Feature Selection for Text CategorizationICDSMLA 202110.1007/978-981-19-5936-3_26(281-292)Online publication date: 7-Feb-2023
https://doi.org/10.1007/978-981-19-5936-3_26
Zhang PBai GHuang ZXu XMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Machine Unlearning for Image RetrievalProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548378(237-245)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548378
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Zhang LWu H(2022)Cosaliency Detection and Region-of-Interest Extraction via Manifold Ranking and MRF in Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.307944160(1-17)Online publication date: 2022
https://doi.org/10.1109/TGRS.2021.3079441
Liang JZhu PDang CHu Q(2022)Semisupervised Laplace-Regularized Multimodality Metric LearningIEEE Transactions on Cybernetics10.1109/TCYB.2020.302227752:5(2955-2967)Online publication date: May-2022
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Zhang LShang YLi PLuo HShao L(2022)Community-Aware Photo Quality Evaluation by Deeply Encoding Human PerceptionIEEE Transactions on Cybernetics10.1109/TCYB.2019.293731952:5(3136-3146)Online publication date: May-2022
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Rios MCalder JLerman G(2022)Analysis and algorithms for ℓ-based semi-supervised learning on graphsApplied and Computational Harmonic Analysis10.1016/j.acha.2022.01.004Online publication date: Jan-2022
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