Skip to main content
SpringerLink
Log in

Group sparse representation for image categorization and semantic video retrieval

  • Research Papers
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Multimedia content analysis and management are a promising and challenging theme. In this paper we develop a novel approach to image representation, which we call group sparse representation (GSR), for image classification and video retrieval. The basic idea is to represent a test image as a weighted combination of all the training images. In particular, we introduce two sets of weight coefficients, one for each training image and the other for each class. Moreover, we formulate our concern as a group nonnegative garrote model. The resulting representations are sparse, and they are appropriate for discriminant analysis. Experiments on Caltech101 and PASCAL VOC2008 image dataset and TRECVID2005 video corpus testify that our proposed approach is efficient and effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory, 2006, 52: 489–509

    Article  MathSciNet  Google Scholar 

  2. Candes E, Tao T. Near optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans Inform Theory, 2006, 52: 5406–5425

    Article  MathSciNet  Google Scholar 

  3. Donoho D. Compressed sensing. IEEE Trans Inform Theory, 2006, 52: 1289–1306

    Article  MathSciNet  Google Scholar 

  4. Donoho D, Tanner J. Thresholds for the recovery of sparse solutions via l1 minimization. In: Conference on Information Sciences and Systems. 2006. 202–206

  5. Goyal V K, Fletcher A K, Rangan S. Compressive sampling and lossy compression. IEEE Signal Proc Mag, 2008, 25: 48–56

    Article  Google Scholar 

  6. Kwak N. Principal component analysis based on l1-norm maximization. IEEE Trans Pattern Anal, 2008, 30: 1672–1680

    Article  Google Scholar 

  7. Elad M. Optimized projections for compressed sensing. IEEE Trans Signal Proces, 2007, 55: 5695–5702

    Article  MathSciNet  Google Scholar 

  8. Yang J C, Wright J, Huang T, et al. Image supre-resolution as sparse representation of raw image patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008). 2008. 1–8

  9. Han Y H, Wu F, Jia J Z, et al. Multi-task sparse discriminant analysis (MtSDA) with overlapping categories. In: Proceeding of the 24th Conference on Artificial Intelligence (AAAI). 2010

  10. Han Y H, Wu F, He X F, et al. Multi-label transfer learning with sparse representation. IEEE Trans Circ Syst Vid, 2010, 20: 1110–1121

    Article  Google Scholar 

  11. Han Y H, Wu F, Tian Q, et al. Multi-label boosting for image annotation by structural grouping sparsity. In: Proceedings of the International Conference on Multimedia (MM’ 10). New York: ACM. 15–24

  12. Sen P, Darabi S. Compressive dual photography. Comput Graph Forum, 2009, 28: 609–618

    Article  Google Scholar 

  13. Wright J, Yang A, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal, 2009, 31: 201–227

    Article  Google Scholar 

  14. Tibshirani R. Regression shrinkage and selection via the lasso. J Roy Stat Soc B, 1996, 58: 267–288

    MathSciNet  MATH  Google Scholar 

  15. Meinshausen N, Buhlmann P. High-dimensional graphs and variable selection with the Lasso. Ann Stat, 2006, 34: 1436–1462

    Article  MathSciNet  MATH  Google Scholar 

  16. Breiman L. Better subset regression using the nonnegative garrote. Technometrics, 1995, 37: 373–384

    Article  MathSciNet  MATH  Google Scholar 

  17. Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J Roy Stat Soc B, 2006, 68: 49–67

    Article  MathSciNet  MATH  Google Scholar 

  18. Yuan M, Lin Y. On the non-negative garrotte estimator. J Roy Stat Soc B, 2007, 69: 143–161

    Article  MathSciNet  MATH  Google Scholar 

  19. Ferrari V, Tuytelaars T, Gool L V. Simultaneous object recognition and segmentation by image exploration. In: Proceedings of 8th European Conference on Computer Vision. 2004. 40–54

  20. Grauman K, Darrell T. Efficient image matching with distributions of local invariant features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005). 2005. 627–634

  21. Lazebnik S, Schmid C, Ponce J. A sparse texture representation using local affine regions. IEEE Trans Pattern Anal, 2005, 27: 1265–1278

    Article  Google Scholar 

  22. Schmid C, Mohr R. Local grayscale invariants for image retrieval. IEEE Trans Pattern Anal, 1997, 19: 530–534

    Article  Google Scholar 

  23. Fergus R, Perona P, Zisserman A. Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003). 2003. 264–271

  24. Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Trans Pattern Anal, 2005, 27: 1615–1630

    Article  Google Scholar 

  25. Lowe D. Distinctive image features from scale-invariant keypoints. Int J Comput Vision, 2004, 60: 1150–1157

    Article  Google Scholar 

  26. Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape context. IEEE Trans Pattern Anal, 2002, 2: 509–522

    Article  Google Scholar 

  27. Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004). 2004. 511–517

  28. Freeman W, Adelson E. The design and use of steerable filters. IEEE Trans Pattern Anal, 1991, 13: 891–906

    Article  Google Scholar 

  29. Lowe D. Object recognition from local scale-invariant features. In: Proceedings of IEEE International Conference on Computer Vision. 1999. 1150–1157

  30. Loncomilla P, Ruiz-del-Solar J. Improving SIFT-based object recognition for robot applications. LNCS, 2005, 3617: 1084–1092

    Google Scholar 

  31. Xing J, Miao Z J. An improved algorithm on image stitching based on SIFT features. In: 2nd International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). 2007. 453

  32. Wu C C, Clipp B, Li X W, et al. 3D model matching with viewpoint-invariant patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008). 2008. 1–8

  33. Kim T K, Cipolla R. Gesture recognition under small sample size. LNCS, 2007, 4843: 335–344

    Google Scholar 

  34. Battiato S, Gallo G, Puglisi G, et al. SIFT features tracking for video stabilization. In: Proceedings of the 14th International Conference on Image Analysis and Processing (ICIAP 2007). 2007. 825–830

  35. Dorko G, Schmid C. Selection of scale-invariant parts for object class recognition. In: IEEE Conference on Computer Vision (ICCV). 2003. 634–639

  36. Sivic J, Russell B, Efros A, et al. Discovering object categories in image collections. In: IEEE Conference on Computer Vision (ICCV). 2005. 370–377

  37. Li F F, Perona P. A bayesian hierarchical model for learning natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2005. 524–531

  38. Yang J, Jiang Y G, Hauptmann A G, et al. Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval (MIR’ 07). 2007. 197–206

  39. Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2006. 2169–2178

  40. Sivic J, Zisserman A. Video google: a text retrieval approach to object matching in videos. In: IEEE Conference on Computer Vision (ICCV). 2003. 1470–1477

  41. Nowak E, Jurie E, Triggs B. Sampling strategies for bag-of-features image classification. In: European Conference on Computer Vision (ECCV). 2006. 490–503

  42. Frey B J, Dueck D. Clustering by passing messages between data points. Science, 2007, 315: 972–976

    Article  MathSciNet  Google Scholar 

  43. Bosch A, Zisserman A, Munoz X. Scene classification using a hybrid generative/dicriminative approach. IEEE Trans Pattern Anal, 2008, 30: 712–727

    Article  Google Scholar 

  44. Yang J C, Yu K, Gong Y H, et al. Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2009. 1794–1801

  45. Wang C H, Yan S C, Zhang L, et al. Multi-label sparse coding for automatic image annotation. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR). 2009. 1643–1650

  46. Morioka N, Satoh S. Learning directional local pairwise bases with sparse coding. In: Proceedings of the British Machine Vision Conference (BMVC). 2010. 32.1–32.11

  47. Lu A, Hou X W, Chen X L, et al. Insect species recognition using sparse representation. In: Proceedings of the British Machine Vision Conference (BMVC). 2010. 108.1–108.10

  48. Vapnik V. Statistical Learning Theory. New York: John Wiley and Sons, 1998. 421–427

    MATH  Google Scholar 

  49. Hastie T, Tishirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer-Verlag, 2001. 19–27

    MATH  Google Scholar 

  50. Golub G H, Loan C F V. Matrix Computations. 3rd ed. Baltimore: Johns Hopkins University Press, 1996. 49–50

    MATH  Google Scholar 

  51. Efron B, Johnstone I, Hastie T, et al. Least angle regression. Ann Stat(with discussions), 2004, 32: 407–499

    MathSciNet  MATH  Google Scholar 

  52. Friedman J H, Hastie T, Hoefling H, et al. Pathwise coordinate optimization. Ann Appl Stat, 2007, 2: 302–332

    Article  Google Scholar 

  53. Amaldi E, Kann V. On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci, 1998, 209: 237–260

    Article  MathSciNet  MATH  Google Scholar 

  54. Donoho D L. For most large underdetermined systems of equations, the minimal l1-norm near-solution approximates the sparsest near-solution. Commun Pur Appl Math, 2006, 59: 907–934

    Article  MathSciNet  Google Scholar 

  55. Li F F, Fergus R, Perona P. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: IEEE Conference Computer Vision and Pattern Recognition (CVPR), Workshop on Generative-Model Based Vision. 2004. 178

  56. Everingham M, Van Gool, L, Williams C K, et al. The PASCAL visual object classes challenge 2008 (VOC2008) results. http://www.pascal-network.org/challenges/VOC/voc2008/workshop/index.html

  57. TREVID. http://www-nlpir.nist.gov/projects/trevid/.

  58. LSCOM Lexicon Definitions and Annotations Version 1.0. Columbia University ADVENT Technical Report 117-2006-3. 2006

  59. Xia D Y, Wu F, Zhang X Q, et al. Local and global approaches of affinity propagation clustering for large scale data. J Zhejiang Univ Sci A, 2008, 9: 1373–1381

    Article  MATH  Google Scholar 

  60. Candes E, Romberg J. l1-magic: Recovery of sparse signals via convex programming. http://www.acm.caltech.edu/l1magic/.2005

  61. Lewis D D. Naive Bayes at forty: the independence assumption in information retrieval. In: 10th European Conference on Machine Learning (ECML-98). 1998. 4–15

  62. Liu Y N, Wu F, Zhang Z H, et al. Sparse representation using nonnegative curds and whey. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2010. 3578–3585

Download references

Author information

Authors and Affiliations

  1. School of Information, Zhejiang University of Finance & Economics, Hangzhou, 310018, China

    YaNan Liu

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310012, China

    YaNan Liu, Fei Wu & YueTing Zhuang

Authors
  1. YaNan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YueTing Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to YaNan Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, Y., Wu, F. & Zhuang, Y. Group sparse representation for image categorization and semantic video retrieval. Sci. China Inf. Sci. 54, 2051–2063 (2011). https://doi.org/10.1007/s11432-011-4344-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4344-2

Keywords

Search

Navigation