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Multi-label Classification for Image Annotation via Sparse Similarity Voting

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Book cover Computer Vision – ACCV 2010 Workshops (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6469))

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Abstract

We present a supervised multi-label classification method for automatic image annotation. Our method estimates the annotation labels for a test image by accumulating similarities between the test image and labeled training images. The similarities are measured on the basis of sparse representation of the test image by the training images, which avoids similarity votes for irrelevant classes. Besides, our sparse representation-based multi-label classification can estimate a suitable combination of labels even if the combination is unlearned. Experimental results using the PASCAL dataset suggest effectiveness for image annotation compared to the existing SVM-based multi-labeling methods. Nonlinear mapping of the image representation using the kernel trick is also shown to enhance the annotation performance.

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References

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

    Google Scholar 

  2. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) Proceedings of ECML-1998, 10th European Conference on Machine Learning, pp. 137–142. Springer, Heidelberg (1998)

    Google Scholar 

  3. Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)

    Article  Google Scholar 

  4. Kressel, U.H.G.: Pairwise classification and support vector machines. MIT Press, Cambridge (1999)

    Google Scholar 

  5. Bucak, S.S., Mallapragada, P.K., Jin, R., Jain, A.K.: Efficient multi-label ranking for multi-class learning: approach to object recognition. In: International Conference on Computer Vision (2009)

    Google Scholar 

  6. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 210–227 (2009)

    Article  Google Scholar 

  7. Wang, C., Yan, S., Zhang, L., Zhang, H.J.: Multi-label sparse coding for automatic image annotation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 0, pp. 1643–1650 (2009)

    Google Scholar 

  8. Hsu, D., Kakade, S., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. In: 23rd Annual Conference on Neural Information Processing Systems (2009)

    Google Scholar 

  9. Donoho, D.: Compressed sensing. IEEE Trans. Information Theory 52, 1289–1306 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  10. Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52, 489–509 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Candès, E.J., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. on Pure and Applied Math. 59, 1207–1223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  12. Candès, E.J.: The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique 346, 589–592 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  13. Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Processing Magazine, 21–30 (March 2008)

    Google Scholar 

  14. Gribonval, R., Nielsen, M.: Sparse representations in unions of bases. IEEE Transactions on Information Theory 49, 3320–3325 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  15. Donoho, D., Elad, M.: Optimally sparse representation in general (non-orthogonal) dictionaries via l 1 minimization. Proc. the National Academy of Sciences of the United States of America, 2197–2202 (2003)

    Google Scholar 

  16. Candès, E.J., Tao, T.: Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory 52, 5406–5425 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  17. Candès, E.J., Tao, T.: Decoding by linear programming. IEEE Transactions on Information Theory 51, 4203–4215 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  18. Rudelson, M., Vershynin, R., Rudelson, M., Vershynin, R.: Geometric approach to error correcting codes and reconstruction of signals. Int. Math. Res. Not. 64, 4019–4041 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  19. Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20, 33–61 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  20. Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 58, 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

  21. Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l 1-regularized least squares. IEEE Journal on Selected Topics in Signal Processing 1, 606–617 (2007)

    Article  Google Scholar 

  22. Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE Journal of Selected Topics in Signal Processing 1, 586–597 (2007)

    Article  Google Scholar 

  23. Tomioka, R., Sugiyama, M.: Dual augmented lagrangian method for efficient sparse reconstruction. Technical report, arXiv:0904.0584, (preprint, 2009)

    Google Scholar 

  24. Pati, Y.C., Rezaiifar, R., Rezaiifar, Y.C.P.R., Krishnaprasad, P.S.: Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers, pp. 40–44 (1993)

    Google Scholar 

  25. Tropp, J.A., Anna, G.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Information Theory 53, 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  26. Needell, D., Vershynin, R.: Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Foundations of Computational Mathematic 9, 317–334 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  27. Needell, D., Tropp, J.A.: CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Applied and Computational Harmonic Analysis 26, 301–321 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  28. Mallat, S., Zhang, Z.: Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing 41, 3397–3415 (1993)

    Article  MATH  Google Scholar 

  29. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88, 303–338 (2010)

    Article  Google Scholar 

  30. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Nagasaki University, Japan

    Tomoya Sakai

  2. Graduate School of Science and Technology, Chiba University, Japan

    Hayato Itoh

  3. Institute of Media and Information Technology, Chiba University, Japan

    Atsushi Imiya

Authors
  1. Tomoya Sakai
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  2. Hayato Itoh
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  3. Atsushi Imiya
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Editor information

Editors and Affiliations

  1. Computer Science Institute, Christian-Albrechts-University Kiel, Germany, Olshausenstr. 40, 24098, Kiel, Germany

    Reinhard Koch

  2. Institute of Computer Science and Information Engineering, National Ilan University, Shen-Lung Rd. 1, 26047, Yi-Lan, Taiwan R.O.C.

    Fay Huang

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© 2011 Springer-Verlag Berlin Heidelberg

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Sakai, T., Itoh, H., Imiya, A. (2011). Multi-label Classification for Image Annotation via Sparse Similarity Voting. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_35

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  • DOI: https://doi.org/10.1007/978-3-642-22819-3_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22818-6

  • Online ISBN: 978-3-642-22819-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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