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Sparse Representation, Modeling and Learning in Visual Recognition

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

In this chapter, the concepts of sparse representation, modeling, and learning are outlined. Sparse representation consists of two basic tasks, data sparsification and encoding feature. Sparse modeling is to model specific tasks by jointly using different disciplines and their sparse properties. Sparse learning is to learn mapping from input signals/features to output by either representing the sparsity of signals/features or modeling the sparsity constraints as regularization items in optimization equations. Then, the fundamentals of visual recognition from feature representation and learning, distance matrix learning, and classification are given. Lastly, the concepts of sparse representation and learning-based classification and other applications of compressive sensing are described.

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References

  1. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891–923 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  2. Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1167–1183 (2002)

    Article  Google Scholar 

  3. Bani Asadi, N., Rish, I., Scheinberg, K., Kanevsky, D., Ramabhadran, B.: Map approach to learning sparse Gaussian Markov networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2009)

    Google Scholar 

  4. Barrow, H.G., Tenenbaum, J.M.: Interpreting line drawings as three-dimensional surfaces. Artif. Intell. 17(1), 75–116 (1981)

    Article  Google Scholar 

  5. Bell, R.M., Koren, Y.: Lessons from the netflix prize challenge. ACM SIGKDD Explor. Newsl. 9(2), 75–79 (2007)

    Article  Google Scholar 

  6. Berry, M.W., Browne, M., Langville, A.N., Pauca, V.P., Plemmons, R.J.: Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 52(1), 155–173 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  7. Bishop, C.M., Tipping, M.E.: Bayesian image super resolution. US Patent 7,106,914 (2006)

    Google Scholar 

  8. Boix, X., Gygli, M., Roig, G., Van Gool, L.: Sparse quantization for patch description. In: IEEE CVPR (2013)

    Google Scholar 

  9. Boix, X., Roig, G., Leistner, C., Van Gool, L.: Nested sparse quantization for efficient feature coding. In: ECCV. Springer (2012)

    Google Scholar 

  10. Bruce, N., Tsotsos, J.: Saliency based on information maximization. In: Advances in Neural Information Processing Systems (2005)

    Google Scholar 

  11. Candès, E.J.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians (2006)

    Google Scholar 

  12. Carroll, M.K., Cecchi, G.A., Rish, I., Garg, R., Rao, A.R.: Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 44(1), 112–122 (2009)

    Article  Google Scholar 

  13. Cawley, G.C., Talbot, N.L.: Gene selection in cancer classification using sparse logistic regression with bayesian regularization. Bioinformatics 22(19), 2348–2355 (2006)

    Article  Google Scholar 

  14. Chan, A.B., Vasconcelos, N., Lanckriet, G.R.: Direct convex relaxations of sparse SVM. In: ICML (2007)

    Google Scholar 

  15. Chandalia, G., Rish, I.: Blind source separation approach to performance diagnosis and dependency discovery. In: ACM SIGCOMM Conference on Internet Measurement (2007)

    Google Scholar 

  16. Cheng, H., Liu, Z., Hou, L., Yang, J.: Sparsity induced similarity measure and its applications. IEEE Trans. Circuits Syst. Video Technol. (2012)

    Google Scholar 

  17. Cheng, H., Liu, Z., Yang, L.: Sparsity induced similarity measure for label propagation. In: IEEE ICCV (2009)

    Google Scholar 

  18. Cho, K.: Simple sparsification improves sparse denoising autoencoders in denoising highly noisy images. In: ICML (2013)

    Google Scholar 

  19. Cong, F., Phan, A.H., Zhao, Q., Huttunen-Scott, T., Kaartinen, J., Ristaniemi, T., Lyytinen, H., Cichocki, A.: Benefits of multi-domain feature of mismatch negativity extracted by non-negative tensor factorization from EEG collected by low-density array. Int. J. Neural Syst. 22(06), 1–19 (2012)

    Article  Google Scholar 

  20. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE CVPR (2005)

    Google Scholar 

  21. d’Aspremont, A., Bach, F., Ghaoui, L.E.: Optimal solutions for sparse principal component analysis. J. Mach. Learn. Res. 9, 1269–1294 (2008)

    MATH  MathSciNet  Google Scholar 

  22. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  23. Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)

    Article  MathSciNet  Google Scholar 

  24. Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE CVPR (2009)

    Google Scholar 

  25. Elhamifar, E., Vidal, R.: Sparse subspace clustering: Algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)

    Article  Google Scholar 

  26. Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10), 1327–1344 (2004)

    Article  Google Scholar 

  27. Fei-Fei, L., Fergus, R., Torralba, A.: Recognizing and learning object categories. CVPR Short Course 106(1), 59–70 (2007)

    Google Scholar 

  28. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE CVPR (2008)

    Google Scholar 

  29. Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vis. 40(1), 25–47 (2000)

    Article  MATH  Google Scholar 

  30. Friedman, J.H.: Fast sparse regression and classification. Int. J. Forecast. 28(3), 722–738 (2012)

    Article  Google Scholar 

  31. Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Joint map registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process. 6(12), 1621–1633 (1997)

    Article  Google Scholar 

  32. Harel, J., Koch, C., Perona, P.: Saliency map tutorial (2012)

    Google Scholar 

  33. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Advances in Neural Information Processing Systems (2006)

    Google Scholar 

  34. He, J., Li, M., Zhang, H.J., Tong, H., Zhang, C.: Manifold-ranking based image retrieval. In: ACM International Conference on Multimedia (2004)

    Google Scholar 

  35. He, X., King, O., Ma, W.Y., Li, M., Zhang, H.J.: Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Trans. Circuits Syst. Video Technol. 13(1), 39–48 (2003)

    Article  Google Scholar 

  36. He, X., Ma, W.Y., Zhang, H.J.: Learning an image manifold for retrieval. In: ACM International Conference on Multimedia (2004)

    Google Scholar 

  37. Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE CVPR (2007)

    Google Scholar 

  38. Imamoglu Konuskan, F.: Visual saliency and biological inspired text detection. Ph.D. thesis, Technical University Munich & California Institute of Technology (2008)

    Google Scholar 

  39. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  40. Jenatton, R., Mairal, J., Bach, F.R., Obozinski, G.R.: Proximal methods for sparse hierarchical dictionary learning. In: ICML (2010)

    Google Scholar 

  41. Ji, S., Carin, L.: Bayesian compressive sensing and projection optimization. In: International Conference on Machine Learning (2007)

    Google Scholar 

  42. Karaoglu, S., Van Gemert, J.C., Gevers, T.: Object reading: text recognition for object recognition. In: ECCV. Springer (2012)

    Google Scholar 

  43. Kato, T., Hino, H., Murata, N.: Sparse coding approach for multi-frame image super resolution (2014) arXiv preprint

    Google Scholar 

  44. Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. In: Matters of Intelligence (1987)

    Google Scholar 

  45. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)

    Article  Google Scholar 

  46. Liu, Y., Jin, R., Yang, L.: Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 421 (2006)

    Google Scholar 

  47. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  48. Mairal, J., Leordeanu, M., Bach, F., Hebert, M., Ponce, J.: Discriminative sparse image models for class-specific edge detection and image interpretation. In: ECCV. Springer (2008)

    Google Scholar 

  49. Mairal, J., Sapiro, G., Elad, M.: Multiscale sparse image representationwith learned dictionaries. In: IEEE ICIP (2007)

    Google Scholar 

  50. Meinshausen, N., Bühlmann, P.: High-dimensional graphs and variable selection with the Lasso. Ann. Stat. 34, 1436–1462 (2006)

    Article  MATH  Google Scholar 

  51. Müller, H., Pun, T., Squire, D.: Learning from user behavior in image retrieval: application of market basket analysis. Int. J. Comput. Vis. 56(1–2), 65–77 (2004)

    Article  Google Scholar 

  52. Negahban, S., Yu, B., Wainwright, M.J., Ravikumar, P.K.: A unified framework for high-dimensional analysis of \(m\)-estimators with decomposable regularizers. In: Advances in Neural Information Processing Systems (2009)

    Google Scholar 

  53. Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics (2003)

    Google Scholar 

  54. Park, M.Y., Hastie, T.: L1-regularization path algorithm for generalized linear models. J. R. Stat. Soc. 69(4), 659–677 (2007)

    Article  MathSciNet  Google Scholar 

  55. Qiu, G.: Indexing chromatic and achromatic patterns for content-based colour image retrieval. Pattern Recognit. 35(8), 1675–1686 (2002)

    Article  MATH  Google Scholar 

  56. Ravikumar, P., Wainwright, M.J., Lafferty, J.D., et al.: High-dimensional ising model selection using \(\ell \)1-regularized logistic regression. Ann. Stat. 38(3), 1287–1319 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  57. Rish, I., Grabarnik, G.: Sparse signal recovery with exponential-family noise. In: Compressed Sensing and Sparse Filtering (2014)

    Google Scholar 

  58. Ryali, S., Supekar, K., Abrams, D.A., Menon, V.: Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage 51(2), 752–764 (2010)

    Article  Google Scholar 

  59. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  60. Shahab, A., Shafait, F., Dengel, A., Uchida, S.: How salient is scene text? In: IAPR International Workshop on Document Analysis Systems (2012)

    Google Scholar 

  61. Shen, H., Huang, J.Z.: Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 99(6), 1015–1034 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  62. Shen, X., Wu, Y.: A unified approach to salient object detection via low rank matrix recovery. In: IEEE CVPR (2012)

    Google Scholar 

  63. Siagian, C., Itti, L.: Biologically inspired mobile robot vision localization. IEEE Trans. Robot. 25(4), 861–873 (2009)

    Article  Google Scholar 

  64. Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20(6), 1529–1542 (2011)

    Article  MathSciNet  Google Scholar 

  65. Sun, J., Zheng, N.N., Tao, H., Shum, H.Y.: Image hallucination with primal sketch priors. In: IEEE CVPR (2003)

    Google Scholar 

  66. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58, 267–288 (1996)

    MATH  MathSciNet  Google Scholar 

  67. Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001)

    MATH  MathSciNet  Google Scholar 

  68. Tosic, I., Frossard, P.: Dictionary learning. IEEE Signal Process. Mag. 28(2), 27–38 (2011)

    Article  Google Scholar 

  69. Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)

    Article  Google Scholar 

  70. Wang, L., Cheng, H., Liu, Z., Zhu, C.: A robust elastic net approach for feature learning. J. Vis. Commun. Image Represent. 25(2), 313–321 (2014)

    Article  Google Scholar 

  71. Williams, O., Blake, A., Cipolla, R.: Sparse bayesian learning for efficient visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1292–1304 (2005)

    Article  Google Scholar 

  72. Wipf, D.P., Rao, B.D.: Sparse bayesian learning for basis selection. IEEE Trans. Signal Process. 52(8), 2153–2164 (2004)

    Article  MathSciNet  Google Scholar 

  73. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  74. Yan, J., Zhu, M., Liu, H., Liu, Y.: Visual saliency detection via sparsity pursuit. IEEE Signal Process. Lett. 17(8), 739–742 (2010)

    Article  Google Scholar 

  75. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: IEEE CVPR (2008)

    Google Scholar 

  76. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  77. Yang, L., Zheng, N., Yang, J., Chen, M., Chen, H.: A biased sampling strategy for object categorization. In: IEEE ICCV (2009)

    Google Scholar 

  78. Zhu, J., Rosset, S., Hastie, T., Tibshirani, R.: \(\ell _1\)-norm support vector machines. Adv. Neural Inf. Process. Syst. 16(1), 49–56 (2004)

    Google Scholar 

  79. Zhuang, L., Chan, T.H., Yang, A.Y., Sastry, S.S., Ma, Y.: Sparse illumination learning and transfer for single-sample face recognition with image corruption and misalignment (2014) arXiv preprint

    Google Scholar 

  80. Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. 67(2), 301–320 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  81. Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. J. Comput. Graph. Stat. 15(2), 265–286 (2006)

    Article  MathSciNet  Google Scholar 

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  1. University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Hong Cheng

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Cheng, H. (2015). Introduction. In: Sparse Representation, Modeling and Learning in Visual Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6714-3_1

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  • DOI: https://doi.org/10.1007/978-1-4471-6714-3_1

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