research-article

Variational inference with graph regularization for image annotation

Authors:

Deng CaiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 2, Issue 2

Article No.: 11, Pages 1 - 21

https://doi.org/10.1145/1899412.1899415

Published: 24 February 2011 Publication History

Abstract

Image annotation is a typical area where there are multiple types of attributes associated with each individual image. In order to achieve better performance, it is important to develop effective modeling by utilizing prior knowledge. In this article, we extend the graph regularization approaches to a more general case where the regularization is imposed on the factorized variational distributions, instead of posterior distributions implicitly involved in EM-like algorithms. In this way, the problem modeling can be more flexible, and we can choose any factor in the problem domain to impose graph regularization wherever there are similarity constraints among the instances. We formulate the problem formally and show its geometrical background in manifold learning. We also design two practically effective algorithms and analyze their properties such as the convergence. Finally, we apply our approach to image annotation and show the performance improvement of our algorithm.

References

[1]

Attias, H. 2000. A variational Bayesian framework for graphical models. Adv. Neural Info. Proc. Syst. 12, 209--215.

[2]

Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D. M., and Jordan, M. I. 2003. Matching words and pictures. J. Mach. Learn. Res. 3, 1107--1135.

Digital Library

[3]

Belkin, M. and Niyogi, P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the Conference on Advances in Neural Information Processing Systems 14, 585--591.

[4]

Belkin, M., Niyogi, P., and Sindhwani, V. 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399--2434.

Digital Library

[5]

Bilmes, J. 2004. On virtual evidence and soft evidence in Bayesian networks. Tech. rep. UWEETR-2004-0016, Department of EE, University of Washington.

[6]

Bishop, C. M. 2007. Pattern Recognition and Machine Learning. Springer.

[7]

Blei, D., Ng, A., and Jordan, M. 2003. Latent dirichlet allocation. J. Mach. Learn. Res.

Digital Library

[8]

Blei, D. M. and Jordan, M. I. 2003. Modeling annotated data. In Proceedings of the ACM International Conference on Research and Development in Informaion Retrieval (ACM SIGIR). 127--134.

Digital Library

[9]

Bousquet, O., Boucheron, S., and Lugosi, G. 2003. Introduction to statistical learning theory. In Advanced Lectures on Machine Learning. 169--207.

[10]

Cai, D., Mei, Q., Han, J., and Zhai, C. 2008. Modeling hidden topics on document manifold. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM'08). 911--920.

Digital Library

[11]

Chang, E. and Sychay, G. 2003. CBSA: Content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Trans. Circ. Syst. Video Tech. 13, 26--38.

Digital Library

[12]

Chang, J. and Blei, D. 2009. Relational topic models for document networks. In Proceedings of Conference on AI and Statistics.

[13]

Chung, F. R. K. 1997. Spectral Graph Theory. Regional Conference Series in Mathematics, vol. 92. AMS.

[14]

Csurka, G., Dance, C. R., Fan, L., Willamowski, J., and Bray, C. 2004. Visual categorization with bags of keypoints. In Proceedings of the Workshop on Statistical Learning in Computer Vision (ECCV). 1--22.

[15]

Hastie, T., Tibshirani, R., and Friedman, J. H. 2001. The Elements of Statistical Learning. Springer-Verlag.

[16]

He, X. 2010. Laplacian regularized d-optimal design for active learning and its application to image retrieval. Trans. Img. Proc. 19, 1, 254--263.

Digital Library

[17]

He, X., Cai, D., Shao, Y., Bao, H., and Han, J. 2009a. Laplacian regularized gaussian mixture model for data clustering. IEEE Trans. Knowl. Data Engin.

Digital Library

[18]

He, X., Ji, M., and Bao, H. 2009b. Graph embedding with constraints. In Proceedings of the 21st International Jont Conference on Artifical Intelligence (IJCAI'09). Morgan Kaufmann Publishers Inc., San Francisco, CA, 1065--1070.

Digital Library

[19]

He, X., Ji, M., and Bao, H. 2009c. A unified active and semi-supervised learning framework for image compression. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 65--72.

[20]

He, X., Ji, M., and Bao, H. 2009d. A unified active and semi-supervised learning framework for image compression. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 65--72.

[21]

Hofmann, T. 1999. Probabilistic latent semantic indexing. In Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, 50--57.

Digital Library

[22]

Hofmann, T. 2001. Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42, 1-2, 177--196.

Digital Library

[23]

Jordan, M. I., Ed. 1999. Learning in Graphical Models. MIT Press, Cambridge, MA.

Digital Library

[24]

Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. 1999. An introduction to variational methods for graphical models. In Learning in Graphical Models. MIT Press, Cambridge, MA, 105--161.

Digital Library

[25]

Li, L.-J., Socher, R., and Fei-Fei, L. 2009. Towards total scene understanding:classification, annotation and segmentation in an automatic framework. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]

Mei, Q., Cai, D., Zhang, D., and Zhai, C. 2008. Topic modeling with network regularization. In Proceedings of the ACM International Conference on World Wide Web (WWW'08). 101--110.

Digital Library

[27]

Minka, T. P. 2003. Estimating a dirichlet distribution. http://research.microsoft.com/minka.

[28]

Monay, F. and Gatica-Perez, D. 2003. On image auto-annotation with latent space models. In Proceedings of the ACM International Conference on Multimedia (SIGMM'03). 275--278.

Digital Library

[29]

Monay, F. and Gatica-Perez, D. 2004. pLSA-based image auto-annotation: constraining the latent space. In Proceedings of the ACM International Conference on Multimedia (SIGMM'04). 348--351.

Digital Library

[30]

Neal, R. M. and Hinton, G. E. 1999. A view of the em algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models. 355--368.

Digital Library

[31]

Nocedal, J. and Wright, S. 2006. Numerical Optimization. Springer.

[32]

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA.

Digital Library

[33]

Rubner, Y., Tomasi, C., and Guibas, L. J. 2000. The earth mover's distance as a metric for image retrieval. Int. J. Comput. Vis. 40, 2, 99--121.

Digital Library

[34]

Shao, Y., Zhou, Y., He, X., Cai, D., and Bao, H. 2009. Semi-supervised topic modeling for image annotation. In Proceedings of the 17th ACM International Conference on Multimedia (MM'09). ACM, New York, 521--524.

Digital Library

[35]

Si, S., Tao, D., and Geng, B. 2010. Bregman divergence-based regularization for transfer subspace learning. IEEE Trans. Knowl. Data Engin. 22, 929--942.

Digital Library

[36]

Song, D. and Tao, D. 2010. Biologically inspired feature manifold for scene classification. Trans. Img. Proc. 19, 1, 174--184.

Digital Library

[37]

Stephen, E. E., Fienberg, S., and Lafferty, J. 2004. Mixed membership models of scientific publications. In Proc. National Acad. Sci.

[38]

Tao, D., Li, X., Wu, X., and Maybank, S. J. 2009. Geometric mean for subspace selection. IEEE Trans. Patt. Anal. Mach. Intell. 31, 2, 260--274.

Digital Library

[39]

Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, Berlin.

Digital Library

[40]

Vapnik, V. N. 1998. Statistical Learning Theory. Wiley.

[41]

Winn, J. and Bishop, C. M. 2005. Variational message passing. J. Mach. Learn. Res. 6, 661--694.

Digital Library

[42]

Xing, E. P., Jordan, M. I., and Russell, S. J. 2003. A generalized mean field algorithm for variational inference in exponential families. In Proceedings of the International Conference on Uncertainty in Artificial Intelligence. 583--591.

Digital Library

[43]

Zhang, R., Zhang, Z. M., Li, M., Ma, W.-Y., and Zhang, H.-J. 2005. A probabilistic semantic model for image annotation and multi-modal image retrieva. In Proceedings of the IEEE International Conference on Computer Vision (ICCV'05). 846--851.

Digital Library

[44]

Zhou, T., Tao, D., and Wu, X. 2010. Manifold elastic net: A unified framework for sparse dimension reduction. Data Min. Knowl. Disc.

Digital Library

[45]

Zhu, X., Lafferty, J. and Ghahramani, Z. 2005. Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the International Conference on Machine Learning (ICML'05).

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Wang MLang CLiang LFeng SWang TGao Y(2020)End-to-End Text-to-Image Synthesis with Spatial ConstrainsACM Transactions on Intelligent Systems and Technology10.1145/339170911:4(1-19)Online publication date: 25-May-2020
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Dong YTao DLi X(2015)Nonnegative Multiresolution Representation-Based Texture Image ClassificationACM Transactions on Intelligent Systems and Technology10.1145/27380507:1(1-21)Online publication date: 7-Oct-2015
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Krasovsky AGilron RYeshurun YMukamel R(2014)Differentiating Intended Sensory Outcome from Underlying Motor Actions in the Human BrainThe Journal of Neuroscience10.1523/JNEUROSCI.5435-13.201434:46(15446-15454)Online publication date: 12-Nov-2014
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Index Terms

Variational inference with graph regularization for image annotation
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 2, Issue 2

February 2011

175 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/1899412

Issue’s Table of Contents

Copyright © 2011 ACM.

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Publication History

Published: 24 February 2011

Accepted: 01 July 2010

Revised: 01 May 2010

Received: 01 February 2010

Published in TIST Volume 2, Issue 2

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Cited By

Wang MLang CLiang LFeng SWang TGao Y(2020)End-to-End Text-to-Image Synthesis with Spatial ConstrainsACM Transactions on Intelligent Systems and Technology10.1145/339170911:4(1-19)Online publication date: 25-May-2020
https://dl.acm.org/doi/10.1145/3391709
Dong YTao DLi X(2015)Nonnegative Multiresolution Representation-Based Texture Image ClassificationACM Transactions on Intelligent Systems and Technology10.1145/27380507:1(1-21)Online publication date: 7-Oct-2015
https://dl.acm.org/doi/10.1145/2738050
Krasovsky AGilron RYeshurun YMukamel R(2014)Differentiating Intended Sensory Outcome from Underlying Motor Actions in the Human BrainThe Journal of Neuroscience10.1523/JNEUROSCI.5435-13.201434:46(15446-15454)Online publication date: 12-Nov-2014
https://doi.org/10.1523/JNEUROSCI.5435-13.2014
Zhai DChang HShan SChen XGao W(2012)Multiview Metric Learning with Global Consistency and Local SmoothnessACM Transactions on Intelligent Systems and Technology10.1145/2168752.21687673:3(1-22)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1145/2168752.2168767

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