Computational Visual Media

, Volume 3, Issue 1, pp 83–94 | Cite as

Semi-supervised dictionary learning with label propagation for image classification

Open Access
Research Article
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Abstract

Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semi-supervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

Keywords

semi-supervised learning dictionary learning label propagation image classification 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation for Young Scientists of China (No. 61402289), and the National Science Foundation of Guangdong Province (No. 2014A030313558).

References

  1. [1]
    Elad, M.; Figueiredo, M. A. T.; Ma, Y. On the role of sparse and redundant representations in image processing. Proceedings of the IEEE Vol. 98, No. 6, 972–982, 2010.CrossRefGoogle Scholar
  2. [2]
    Wright, J.; Ma, Y.; Mairal, J.; Sapiro, G.; Huang, T. S.; Yan, S. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE Vol. 98, No. 6, 1031–1044, 2010.CrossRefGoogle Scholar
  3. [3]
    Chen, Y.-C.; Patel, V. M.; Phillips, P. J.; Chellappa, R. Dictionary-based face recognition from video. In: Computer Vision–ECCV 2012. Fitzgibbon, A.; Lazebnik, S.; Perona, P.; Sato, Y.; Schmid, C. Eds. Springer Berlin Heidelberg, 766–779, 2012.CrossRefGoogle Scholar
  4. [4]
    Mairal, J.; Elad, M.; Sapiro, G. Sparse representation for color image restoration. IEEE Transactions on Image Processing Vol. 17, No. 1, 53–69, 2008.MathSciNetCrossRefMATHGoogle Scholar
  5. [5]
    Bryt, O.; Elad, M. Compression of facial images using the K-SVD algorithm. Journal of Visual Communication and Image Representation Vol. 19, No. 4, 270–282, 2008.CrossRefGoogle Scholar
  6. [6]
    Bryt, O.; Elad, M. Improving the k-svd facial image compression using a linear deblocking method. In: Proceedings of the IEEE 25th Convention of Electrical and Electronics Engineers in Israel, 533–537, 2008.Google Scholar
  7. [7]
    Yang, J.; Yu, K.; Huang, T. Supervised translationinvariant sparse coding. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3517–3524, 2010.Google Scholar
  8. [8]
    Yang, M.; Zhang, L.; Yang, J.; Zhang, D. Metaface learning for sparse representation based face recognition. In: Proceedings of the IEEE International Conference on Image Processing, 1601–1604, 2010.Google Scholar
  9. [9]
    Mairal, J.; Ponce, J.; Sapiro, G.; Zisserman, A.; Bach, F. R. Supervised dictionary learning. In: Proceedings of the Advances in Neural Information Processing Systems, 1033–1040, 2009.Google Scholar
  10. [10]
    Zhang, Q.; Li, B. Discriminative K-SVD for dictionary learning in face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2691–2698, 2010.Google Scholar
  11. [11]
    Pham, D.-S.; Venkatesh, S. Joint learning and dictionary construction for pattern recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2008.Google Scholar
  12. [12]
    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 Vol. 31, No. 2, 210–227, 2009.CrossRefGoogle Scholar
  13. [13]
    Aharon, M.; Elad, M.; Bruckstein, A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing Vol. 54, No. 11, 4311–4322, 2006.CrossRefMATHGoogle Scholar
  14. [14]
    Mairal, J.; Bach, F.; Ponce, J.; Sapiro, G.; Zisserman, A. Discriminative learned dictionaries for local image analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–8, 2008.Google Scholar
  15. [15]
    Yang, M.; Dai, D.; Shen, L.; Van Gool, L. Latent dictionary learning for sparse representation based classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4138–4145, 2014.Google Scholar
  16. [16]
    Jiang, Z.; Lin, Z.; Davis, L. S. Learning a discriminative dictionary for sparse coding via label consistent K-SVD. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1697–1704, 2011.Google Scholar
  17. [17]
    Yang, J.; Yu, K.; Gong, Y.; Huang, T. Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1794–1801, 2009.Google Scholar
  18. [18]
    Wang, X.; Guo, X.; Li, S. Z. Adaptively unified semisupervised dictionary learning with active points. In: Proceedings of the IEEE International Conference on Computer Vision, 1787–1795, 2015.Google Scholar
  19. [19]
    Shrivastava, A.; Pillai, J. K.; Patel, V. M.; Chellappa, R. Learning discriminative dictionaries with partially labeled data. In: Proceedings of the 19th IEEE International Conference on Image Processing, 3113–3116, 2012.Google Scholar
  20. [20]
    Jian, M.; Jung, C. Semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation. IEEE Transactions on Multimedia Vol. 18, No. 3, 458–473, 2016.CrossRefGoogle Scholar
  21. [21]
    Wang, D.; Zhang, X.; Fan, M.; Ye, X. Semi-supervised dictionary learning via structural sparse preserving. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2137–2144, 2016.Google Scholar
  22. [22]
    Zhang, G.; Jiang, Z.; Davis, L. S. Online semisupervised discriminative dictionary learning for sparse representation. In: Computer Vision–ACCV 2012. Lee, K. M.; Mu, K.; Matsushita, Y.; Rehg, J. M.; Hu, Z. Eds. Springer Berlin Heidelberg, 259–273, 2012.Google Scholar
  23. [23]
    Babagholami-Mohamadabadi, B.; Zarghami, A.; Zolfaghari, M.; Baghshah, M. S. PSSDL: Probabilistic semi-supervised dictionary learning. In: Machine Learning and Knowledge Discovery in Databases. Blockeel, H.; Kersting, K.; Nijssen, S.; Železný, F. Eds. Springer Berlin Heidelberg, 192–207, 2013.CrossRefGoogle Scholar
  24. [24]
    Yang, M.; Zhang, L.; Feng, X.; Zhang, D. Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the IEEE International Conference on Computer Vision, 543–550, 2011.Google Scholar
  25. [25]
    Zhou, N.; Shen, Y.; Peng, J.; Fan, J. Learning interrelated visual dictionary for object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3490–3497, 2012.Google Scholar
  26. [26]
    Deng, W.; Hu, J.; Guo, J. Extended SRC: Undersampled face recognition via intraclass variant dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 34, No. 9, 1864–1870, 2012.CrossRefGoogle Scholar
  27. [27]
    Zhu, X.; Lafferty, J.; Rosenfeld, R. Semi-supervised learning with graphs. Carnegie Mellon University, Language Technologies Institute, School of Computer Science, 2005.Google Scholar
  28. [28]
    Wang, B.; Tu, Z.; Tsotsos, J. K. Dynamic label propagation for semi-supervised multi-class multilabel classification. In: Proceedings of the IEEE International Conference on Computer Vision, 425–432, 2013.Google Scholar
  29. [29]
    Blum, A.; Mitchell, T. Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, 92–100, 1998.Google Scholar
  30. [30]
    Mallapragada, P. K.; Jin, R.; Jain, A. K.; Liu, Y. SemiBoost: Boosting for semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 31, No. 11, 2000–2014, 2009.CrossRefGoogle Scholar
  31. [31]
    Gong, C.; Tao, D.; Maybank, S. J.; Liu, W.; Kang, G.; Yang, J. Multi-modal curriculum learning for semisupervised image classification. IEEE Transactions on Image Processing Vol. 25, No. 7, 3249–3260, 2016.MathSciNetCrossRefGoogle Scholar
  32. [32]
    Bosch, A.; Zisserman, A.; Munoz, X. Image classification using random forests and ferns. In: Proceedings of the IEEE 11th International Conference on Computer Vision, 1–8, 2007.Google Scholar
  33. [33]
    Xiong, C.; Kim, T.-K. Set-based label propagation of face images. In: Proceedings of the 19th IEEE International Conference on Image Processing, 1433–1436, 2012.Google Scholar
  34. [34]
    Cheng, H.; Liu, Z.; Yang, J. Sparsity induced similarity measure for label propagation. In: Proceedings of the IEEE 12th International Conference on Computer Vision, 317–324, 2009.Google Scholar
  35. [35]
    Kang, F.; Jin, R.; Sukthankar, R. Correlated label propagation with application to multi-label learning. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1719–1726, 2006.Google Scholar
  36. [36]
    Wang, H.; Nie, F.; Cai, W.; Huang, H. Semisupervised robust dictionary learning via efficient lnorms minimization. In: Proceedings of the IEEE International Conference on Computer Vision, 1145–1152, 2013.Google Scholar
  37. [37]
    Martinez, A. M. The AR face database. CVC Technical Report 24, 1998.Google Scholar
  38. [38]
    Lee, K.-C.; Ho, J.; Kriegman, D. J. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 27, No. 5, 684–698, 2005.CrossRefGoogle Scholar
  39. [39]
    LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE Vol. 86, No. 11, 2278–2324, 1998.CrossRefGoogle Scholar
  40. [40]
    Hull, J. J. A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 16, No. 5, 550–554, 1994.CrossRefGoogle Scholar
  41. [41]
    Lazebnik, S.; Schmid, C.; Ponce, J. A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 27, No. 8, 1265–1278, 2005.CrossRefGoogle Scholar
  42. [42]
    Cai, S.; Zuo, W.; Zhang, L.; Feng, X.; Wang, P. Support vector guided dictionary learning. In: Computer Vision–ECCV 2014. Fleet, D.; Pajdla, T.; Schiele, B.; Tuytelaars, T. Eds. Springer International Publishing, 624–639, 2014.Google Scholar
  43. [43]
    Boix, X.; Roig, G.; Van Gool, L. Comment on “Ensemble projection for semi-supervised image classification”. arXiv preprint arXiv:1408.6963, 2014.Google Scholar
  44. [44]
    Dai, D.; Van Gool, L. Ensemble projection for semisupervised image classification. In: Proceedings of the IEEE International Conference on Computer Vision, 2072–2079, 2013.Google Scholar
  45. [45]
    Oliva, A.; Torralba, A. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision Vol. 42, No. 3, 145–175, 2001.CrossRefMATHGoogle Scholar
  46. [46]
    Ojala, T.; Pietikainen, M.; Maenpaa, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 7, 971–987, 2002.CrossRefMATHGoogle Scholar

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© The Author(s) 2016

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Authors and Affiliations

  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina
  3. 3.Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of EducationGuangzhouChina

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