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Analysis of single- and dual-dictionary strategies in pedestrian classification

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Abstract

Sparse coding has recently been a hot topic in visual tasks in image processing and computer vision. It has applications and brings benefits in reconstruction-like tasks and in classification-like tasks as well. However, regarding binary classification problems, there are several choices to learn and use dictionaries that have not been studied. In particular, how single-dictionary and dual-dictionary approaches compare in terms of classification performance is largely unexplored. We compare three single-dictionary strategies and two dual-dictionary strategies for the problem of pedestrian classification (“pedestrian” vs “background” images). In each of these five cases, images are represented as the sparse coefficients induced from the respective dictionaries, and these coefficients are the input to a regular classifier both for training and subsequent classification of novel unseen instances. Experimental results with the INRIA pedestrian dataset suggest, on the one hand, that dictionaries learned from only one of the classes, even from the background class, are enough for obtaining competitive good classification performance. On the other hand, while better performance is generally obtained when instances of both classes are used for dictionary learning, the representation induced by a single dictionary learned from a set of instances from both classes provides comparable or even superior performance over the representations induced by two dictionaries learned separately from the pedestrian and background classes.

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References

  1. Alfaro A, Mery D, Soto A (2016) Action recognition in video using sparse coding and relative features. In: Computer vision and pattern recognition (CVPR), pp 2688–2697

  2. Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using Matthews correlation coefficient metric. PLoS ONE 12(6):e0177678. https://doi.org/10.1371/journal.pone.0177678

    Article  Google Scholar 

  3. Bryt O, Elad M (2008) Compression of facial images using the K-SVD algorithm. J Vis Commun Image Represent 19(4):270–282

    Article  Google Scholar 

  4. Castrodad A, Sapiro G (2012) Sparse modeling of human actions from motion imagery. Int J Comput Vis (IJCV) 100(1):1–15

    Article  Google Scholar 

  5. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer vision and pattern recognition (CVPR)

  6. Deng W, Hu J, Guo J (2012) Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE Trans Pattern Anal Mach Intell (PAMI) 34(9):1864–1870

    Article  Google Scholar 

  7. Deng W, Hu J, Guo J (2013) In defense of sparsity based face recognition. In: Computer vision and pattern recognition (CVPR)

  8. Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, Berlin

    Book  MATH  Google Scholar 

  9. Elad M, Aharon M (2006) Image denoising via learned dictionaries and sparse representation. In: Computer vision and pattern recognition (CVPR)

  10. Fadili MJ, Starck JL, Murtagh F (2009) Inpainting and zooming using sparse representations. Comput J 52:64–79

    Article  Google Scholar 

  11. Gao Y, Ma J, Yuille AL (2017) Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans Image Process 26(5):2545–2560

    Article  MathSciNet  Google Scholar 

  12. Hawe S, Seibert M, Kleinsteuber M (2013) Separable dictionary learning. In: Computer vision and pattern recognition (CVPR), pp 438–445

  13. Howse J, Joshi P, Beyeler M (2016) OpenCV: Computer Vision Projects with Python. Packt

  14. Hsieh SH, Lu CS, Pei SC (2014) 2D sparse dictionary learning via tensor decomposition. In: IEEE global conference on signal and information processing (GlobalSIP), pp 492–496

  15. Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90–95

    Article  Google Scholar 

  16. Jiang Z, Lin Z, Davis LS (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell (PAMI) 35(11):2651–2664

    Article  Google Scholar 

  17. Krishna Vinay G, Haque SM, Venkatesh Babu R, Ramakrishnan K (2012) Human detection using sparse representation. In: IEEE international conference on acoustics, speech and signal processing (ICASSP)

  18. Liang F, Tang S, Zhang Y, Xu Z, Li J (2014) Pedestrian detection based on sparse coding and transfer learning. Mach Vis Appl (MVA) 25(7):1697–1709

    Article  Google Scholar 

  19. Liu W, Tao D, Cheng J, Tang Y (2014) Multiview Hessian discriminative sparse coding for image annotation. Comput Vis Image Underst (CVIU) 118(Supplement C):50–60

    Article  Google Scholar 

  20. Liu W, Liu H, Tao D, Wang Y, Lu K (2015) Multiview Hessian regularized logistic regression for action recognition. Sig Process 110:101–107

    Article  Google Scholar 

  21. Liu W, Zha ZJ, Wang Y, Lu K, Tao D (2016) \(p\)-Laplacian regularized sparse coding for human activity recognition. IEEE Trans Ind Electron 63(8):5120–5129

    Google Scholar 

  22. Liu Y, Lasang P, Siegel M, Sun Q (2016) Multi-sparse descriptor: a scale invariant feature for pedestrian detection. Neurocomputing 184:55–65

    Article  Google Scholar 

  23. Lou Y, Bertozzi AL, Soatto S (2011) Direct sparse deblurring. J Math Imaging Vis 39(1):1–12

    Article  MathSciNet  MATH  Google Scholar 

  24. Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. IEEE Trans Image Process 17(1):53–69

    Article  MathSciNet  MATH  Google Scholar 

  25. Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: International conference on machine learning (ICML)

  26. Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60

    MathSciNet  MATH  Google Scholar 

  27. Mairal J, Bach F, Ponce J (2012) Task-driven dictionary learning. IEEE Trans Pattern Anal Mach Intell (PAMI) 34(4):791–804

    Article  Google Scholar 

  28. Mairal J, Bach F, Ponce J (2014) Sparse modeling for image and vision processing. Found Trends Comput Graph Vis 8(2–3):85–283

    Article  MATH  Google Scholar 

  29. Mallat S, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3397–3415

    Article  MATH  Google Scholar 

  30. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct 405(2):442–451

    Article  Google Scholar 

  31. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  32. Ren X, Ramanan D (2013) Histograms of sparse codes for object detection. In: Computer vision and pattern recognition (CVPR)

  33. Rigamonti R, Brown M, Lepetit V (2011) Are sparse representations really relevant for image classification? In: Computer vision and pattern recognition (CVPR)

  34. Rubinstein R, Zibulevsky M, Elad M (2010) Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans Signal Process 58(3):1553–1564

    Article  MathSciNet  MATH  Google Scholar 

  35. Sahay A (2016) Data visualization, vol I. Business Expert Press, New York

    Google Scholar 

  36. Serra-Toro C, Hernández-Górriz Á, Traver VJ (2017) Strategies of dictionary usages for sparse representations for pedestrian classification. Pattern Recogn Image Anal IbPRIA 2017:96–103

    Article  MathSciNet  Google Scholar 

  37. Shekhar S, Patel VM, Nguyen HV, Chellappa R (2015) Coupled projections for adaptation of dictionaries. IEEE Trans Image Process 24(10):2941–2954

    Article  MathSciNet  Google Scholar 

  38. Shi Q, Eriksson A, van den Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem? In: Computer vision and pattern recognition (CVPR)

  39. Singh K, Vishwakarma DK, Walia GS (2017) Blind image deblurring via gradient orientation-based clustered coupled sparse dictionaries. Pattern Anal Appl (PAA). https://doi.org/10.1007/s10044-017-0652-5

  40. Sironi A, Tekin B, Rigamonti R, Lepetit V, Fua P (2015) Learning separable filters. IEEE Trans Pattern Anal Mach Intell (PAMI) 37(1):94–106

    Article  Google Scholar 

  41. Sivalingam R, Somasundaram G, Morellas V, Papanikolopoulos N, Lotfallah OA, Park Y (2010) Dictionary learning based object detection and counting in traffic scenes. In: International conference on distributed smart cameras

  42. Spratling MW (2014) Classification using sparse representations: a biologically plausible approach. Biol Cybern 108(1):61–73

    Article  MathSciNet  Google Scholar 

  43. Sulam J, Ophir B, Zibulevsky M, Elad M (2016) Trainlets: dictionary learning in high dimensions. IEEE Trans Signal Process 64(12):3180–3193

    Article  MathSciNet  Google Scholar 

  44. Sun R, Zhang G, Yan X, Gao J (2016) Robust pedestrian classification based on hierarchical kernel sparse representation. Sensors 16(8):1296

    Article  Google Scholar 

  45. Wang W, Yan Y, Zhang L, Hong R, Sebe N (2016) Collaborative sparse coding for multiview action recognition. IEEE Multimedia 23(4):80–87

    Article  Google Scholar 

  46. Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83

    Article  Google Scholar 

  47. Wright J et al (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell (PAMI) 31(2):210–227

    Article  Google Scholar 

  48. Wright J et al (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  49. Xie YF, Su SZ, Li SZ (2010) A pedestrian classification method based on transfer learning. In: 2010 International conference on image analysis and signal processing, pp 420–425

  50. Xu R, Jiao J, Zhang B, Ye Q (2012) Pedestrian detection in images via cascaded \(L_1\)-norm minimization learning method. Pattern Recogn 45(7):2573–2583

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  52. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: International conference on computer vision (ICCV), pp 543–550

  53. Yao T, Wang Z, Xie Z, Gao J, Feng DD (2017) Learning universal multiview dictionary for human action recognition. Pattern Recogn 64:236–244

    Article  Google Scholar 

  54. Zhang L, Zhou WD, Chang PC, Liu J, Yan Z, Wang T, Li FZ (2012) Kernel sparse representation-based classifier. IEEE Trans Signal Process 60(4):1684–1695

    Article  MathSciNet  Google Scholar 

  55. Zheng J, Jiang Z, Chellappa R (2016) Cross-view action recognition via transferable dictionary learning. IEEE Trans Image Process 25(6):2542–2556

    Article  MathSciNet  Google Scholar 

  56. Zheng M, Bu J, Chen C, Wang C, Zhang L, Qiu G, Cai D (2011) Graph regularized sparse coding for image representation. IEEE Trans Image Process 20(5):1327–1336

    Article  MathSciNet  MATH  Google Scholar 

  57. Zheng M, Bu J, Chen C (2014) Hessian sparse coding. Neurocomputing 123:247–254

    Article  Google Scholar 

  58. Zhu Q, Yeh M, Cheng K, Avidan S (2006) Fast human detection using a cascade of histograms of oriented gradients. In: Computer vision and pattern recognition (CVPR), pp 1491–1498

  59. Zhu XX, Bamler R (2013) A sparse image fusion algorithm with application to pan-sharpening. IEEE Trans Geosci Remote Sens 51(5):2827–2836

    Article  Google Scholar 

Download references

Acknowledgements

The collaboration of Á. Hernández-Górriz in an earlier stage of this work is acknowledged. This work is partly funded by the Spanish Ministerio de Economía, Industria y Competitividad (TIN2013-46522-P), and Generalitat Valenciana (PROMETEOII/2014/062).

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Authors and Affiliations

  1. Institute of New Imaging Technologies, Jaume-I University, Castellón, Spain

    V. Javier Traver & Carlos Serra-Toro

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  1. V. Javier Traver
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  2. Carlos Serra-Toro
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Correspondence to V. Javier Traver.

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Traver, V.J., Serra-Toro, C. Analysis of single- and dual-dictionary strategies in pedestrian classification. Pattern Anal Applic 21, 655–670 (2018). https://doi.org/10.1007/s10044-018-0704-5

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