skip to main content
research-article

Large-scale multilabel propagation based on efficient sparse graph construction

Published:27 December 2013Publication History
Skip Abstract Section

Abstract

With the popularity of photo-sharing websites, the number of web images has exploded into unseen magnitude. Annotating such large-scale data will cost huge amount of human resources and is thus unaffordable. Motivated by this challenging problem, we propose a novel sparse graph based multilabel propagation (SGMP) scheme for super large scale datasets. Both the efficacy and accuracy of the image annotation are further investigated under different graph construction strategies, where Gaussian noise and non-Gaussian sparse noise are simultaneously considered in the formulations of these strategies. Our proposed approach outperforms the state-of-the-art algorithms by focusing on: (1) For large-scale graph construction, a simple yet efficient LSH (Locality Sensitive Hashing)-based sparse graph construction scheme is proposed to speed up the construction. We perform the multilabel propagation on this hashing-based graph construction, which is derived with LSH approach followed by sparse graph construction within the individual hashing buckets; (2) To further improve the accuracy, we propose a novel sparsity induced scalable graph construction scheme, which is based on a general sparse optimization framework. Sparsity essentially implies a very strong prior: for large scale optimization, the values of most variables shall be zeros when the solution reaches the optimum. By utilizing this prior, the solutions of large-scale sparse optimization problems can be derived by solving a series of much smaller scale subproblems; (3) For multilabel propagation, different from the traditional algorithms that propagate over individual label independently, our proposed propagation first encodes the label information of an image as a unit label confidence vector and naturally imposes inter-label constraints and manipulates labels interactively. Then, the entire propagation problem is formulated on the concept of Kullback-Leibler divergence defined on probabilistic distributions, which guides the propagation of the supervision information. Extensive experiments on the benchmark dataset NUS-WIDE with 270k images and its lite version NUS-WIDE-LITE with 56k images well demonstrate the effectiveness and scalability of the proposed multi-label propagation scheme.

Skip Supplemental Material Section

Supplemental Material

References

  1. Andoni, A. and Indyk, P. 2008. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Comm. ACM 51, 1, 117--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Andrew, G. and Gao, J. 2007. Scalable training of l1-regularized log-linear models. In Proceedings of ICML. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Beck, A. and Teboulle, M. 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Boyd, S. and Vandenberghe, L. 2004. Convex Optimization. Cambridge University Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Cai, J.-F., Candes, E. J., and Shen, Z. 2010. A singular value thresholding algorithm for matrix completion. SIAM J. Optim. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Charikar, M. 2002. Similarity estimation techniques from rounding algorithms. In Proceedings of STOC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Chen, G., Song, Y., Wang, F., and Zhang, C. 2008. Semi-supervised multi-label learning by solving a sylvester equation. In Proceedings of the SIAM International Conference on Data Mining.Google ScholarGoogle Scholar
  8. Chen, S., Donoho, D., and Saunders, M. 2001. Atomic decomposition by basis pursuit. SIAM review. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Chen, X., Mu, Y., Yan, S., and Chua, T. 2010. Efficient large-scale image annotation by probabilistic collaborative multi-label propagation. In Proceedings of ACM MM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Cheng, B., Yang, J., Yan, S., Fu, Y., and Huang, T. S. 2010. Learning with ℓ1-graph for image analysis. IEEE Trans. Image Proc. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., and Zheng, Y.-T. 2009. Nus-wide: A real-world web image database from national university of singapore. In Proceedings of CIVR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Collobert, R., Sinz, F. H., Weston, J., and Bottou, L. 2006. Large scale transductive SVMS. J. Mach. Learn. Rese. 7, 1687--1712. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Cover, T. M. and Thomas, J. A. 1991. Elements of Information Theory. Wiley Series in Telecommunications. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of SCG. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Delalleau, O., Bengio, Y., and Le Roux, N. 2005. Efficient non-parametric function induction in semi-supervised learning. In Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 96--103.Google ScholarGoogle Scholar
  16. Duda, R., Stork, D., and Hart, P. 2000. Pattern Classification. Wiley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Gao, Y., Tang, J., Hong, R., Yan, S., Dai, Q., Zhang, N., and Chua, T. 2012. Camera constraint-free view-based 3D object retrieval. IEEE Trans. Image Proc. 21, 4, 2269--2281.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Hale, E. T., Yin, W., and Zhang, Y. 2008. Fixed-point continuation for l1-minimization: Methodology and convergence. SIAM J. Optim. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Hiriart-Urruty, J. and Lemaréchal, C. 2001. Fundamentals of Convex Analysis. Springer-Verlag.Google ScholarGoogle Scholar
  20. Indyk, P. and Motwani, R. 1998. Approximate nearest neighbors: Towards removing the curse of dimensionality. In Proceedings of the Symposium on Theory Computing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Joachims, T. 2003. Transductive learning via spectral graph partitioning. In Proceedings of the International Conference on Machine Learning (ICML).Google ScholarGoogle Scholar
  22. Karlen, M., Weston, J., Erkan, A., and Collobert, R. 2008. Large-scale manifold transduction. In Proceedings of ICML. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Lee, H., Battle, A., Raina, R., and Ng, A. 2007. Efficient sparse coding algorithms. In Proceedings of NIPS.Google ScholarGoogle Scholar
  24. Liu, J., Ji, S., and Ye, J. 2009. Multi-task feature learning via efficient l2, 11-norm minimization. In Proceedings of UAI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Liu, Y., Jin, R., and Yang, L. 2006. Semi-supervised multi-label learning by constrained non-negative matrix factorization. In Proceedings of AAAI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Mu, Y., Shen, J., and Yan, S. 2010. Weakly-supervised hashing in kernel space. In Proceedings of CVPR.Google ScholarGoogle Scholar
  27. Mu, Y., Wright, J., and Chang, S.-F. 2012. Accelerated large scale optimization by concomitant hashing. In Proceedings of European Conference on Computer Vision (ECCV). Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Perkins, S., Lacker, K., and Theiler, J. 2003. Grafting: Fast, incremental feature selection by gradient descent in function space. J. Mech. Learn. Res. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Qi, G.-J., Hua, X.-S., Rui, Y., Tang, J., Mei, T., and Zhang, H.-J. 2007. Correlative multi-label video annotation. In Proceedings of MM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Roweis, S. and Saul, L. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323--2326.Google ScholarGoogle ScholarCross RefCross Ref
  31. Russell, S. and Norvig, P. 2009. Artificial Intelligence: A Modern Approach. Prentice Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Schmidt, M., Van Den Berg, E., Friedlander, M., and Murphy, K. 2009. Optimizing costly functions with simple constraints: A limited-memory projected quasi-newton algorithm. In Proceedings of UAI.Google ScholarGoogle Scholar
  33. Sindhwani, V. and Keerthi, S. S. 2006. Large scale semi-supervised linear svms. In Proceedings of the 29th Annual International ACM SIGIR (SIGIR'06). Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Subramanya, A. and Bilmes, J. 2008. Soft-supervised learning for text classification. In Proceedings of EMNLP. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Subramanya, A. and Bilmes, J. 2009. Entropic graph regularization in non-parametric semi-supervised classification. In Proceedings of NIPS.Google ScholarGoogle Scholar
  36. Tang, J., Yan, S., Hong, R., Qi, G.-J., and Chua, T.-S. 2009. Inferring semantic concepts from community-contributed images and noisy tags. In Proceedings of MM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. Ser. B (Methodological).Google ScholarGoogle Scholar
  38. Tsang, I. W. and Kwok, J. T. 2006. Large-scale sparsified manifold regularization. In Proceedings of NIPS.Google ScholarGoogle Scholar
  39. Van Den Berg, E. and Friedlander, M. 2008. Probing the Pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Wang, F. and Zhang, C. 2006. Label propagation through linear neighborhoods. In Proceedings of ICML. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Wang, M., Hua, X.-S., Hong, R., Tang, J., Qi, G.-J., and Song, Y. 2012. Unified video annotation via multi-graph learning. IEEE Trans. Circ. Syst. Video Tech. 19, 5, 733--746. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Zha, Z.-J., Mei, T., Wang, J., Wang, Z., and Hua, X.-S. 2009. Graph-based semi-supervised learning with multiple labels. J. Visual Commun. Image Represent. 20, 2, 97--103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Zhang, H., Zha, Z.-J., Yan, S., Wang, M., and Chua, T.-S. 2012. Robust non-negative graph embedding: Towards noisy data, unreliable graphs, and noisy labels. In Proceedings of CVPR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Zhu, X. 2005. Semi-Supervised Learning with Graphs. Carnegie Mellon University.Google ScholarGoogle Scholar
  45. Zhu, X. 2006. Semi-Supervised Learning Literature Survey. Carnegie Mellon University.Google ScholarGoogle Scholar
  46. Zhu, X., Ghahramani, Z., and Lafferty, J. 2003. Semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the International Conference on Machine Learning (ICML).Google ScholarGoogle Scholar

Index Terms

  1. Large-scale multilabel propagation based on efficient sparse graph construction

      Recommendations

      Comments

      Login options

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

      Sign in

      Full Access

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!