Structured learning approach to image descriptor combination

J. Zhou; Z. Fu; A. Robles-Kelly

Structured learning approach to image descriptor combination

Access Full Text

Structured learning approach to image descriptor combination

Author(s): J. Zhou ; Z. Fu ; A. Robles-Kelly
DOI: 10.1049/iet-cvi.2010.0080

For access to this article, please select a purchase option:

Buy article PDF

Buy Knowledge Pack

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership

Recommend Title Publication to library

IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Author(s): J. Zhou ^{1, 2} ; Z. Fu ³ ; A. Robles-Kelly ^{1, 2}
- Affiliations: 1: NICTA, Canberra, Australia
  2: College of Engineering and Computer Science, ANU, Canberra, Australia
  3: Faculty of Information Technology, Monash University, Australia
Source: Volume 5, Issue 2, March 2011, p. 134 – 142
DOI: 10.1049/iet-cvi.2010.0080 , Print ISSN 1751-9632, Online ISSN 1751-9640

Published

In this study, the authors address the problem of combining descriptors for purposes of object categorisation and classification. The authors cast the problem in a structured learning setting by viewing the classifier bank and the codewords used in the categorisation and classification tasks as random fields. In this manner, the authors can abstract the problem into a graphical model setting, in which the fusion operation is a transformation over the field of descriptors and classifiers. Thus, the problem reduces itself to that of recovering the optimal transformation using a cost function which is convex and can be converted into either a quadratic or linear programme. This cost function is related to the target function used in discrete Markov random field approaches. The authors demonstrate the utility of our algorithm for purposes of image classification and learning class categories on two datasets.

References

1. 1)
  - Platt, J.: `Probabilistic outputs for support vector machines and comparison to regularized likelihood methods', Proc. Advances in Large Margin Classifiers, 2000, p. 61–74.
2. 2)
  - Cour, T., Shi, J.: `Solving markov random fields with spectral relaxation', Proc. Int. Conf. on Artificial Intelligence and Statistics, 2007.
3. 3)
  - Shokoufandeh, A., Dickinson, S.J., Siddiqi, K., Zucker, S.W.: `Indexing using a spectral encoding of topological structure', Proc. Computer Vision and Pattern Recognition, 1998, p. 491–497.
4. 4)
  - Bach, F.R., Thibaux, R., Jordan, M.I.: `Computing regularization paths for learning multiple kernels', Proc. Conf. on Neural Information Processing Systems (NIPS), 2004.
5. 5)
  - Nilsback, M.E., Zisserman, A.: `A visual vocabulary for flower classification', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2006, p. 1447–1454.
6. 6)
  - L. Breiman . Random forests. Mach. Learn. , 1 , 5 - 32
7. 7)
  - Varma, M., Ray, D.: `Learning the discriminative powerinvariance trade-off', Proc. IEEE Int. Conf. on Computer Vision, 2007, p. 1–8.
8. 8)
  - P.J. Huber . (1981) Robust statistics.
9. 9)
  - Boykov, Y., Jolly, M.-P.: `Interactive graph cuts for optimal boundary and region segmentation of objects in n–d images', Proc. Int. Conf. on Computer Vision, 2001, p. 105–112.
10. 10)
  - Y. Freund , R.E. Schapire . A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. , 1 , 119 - 139
11. 11)
  - J. Zhang , M. Marszalek , S. Lazebnik , C. Schmid . Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. , 2 , 213 - 238
12. 12)
  - L. Itti , C. Kock , E. Niebur . A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. , 11 , 1254 - 1259
13. 13)
  - Nilsback, M.E., Zisserman, A.: `Automated flower classification over a large number of classes', Proc. 2008 Sixth Indian Conf. on Computer Vision, Graphics and Image Processing, 2008, p. 722–729.
14. 14)
  - N. Vasconcelos . On the efficient evaluation of probabilistic similarity functions for image retrieval. IEEE Trans. Inf. Theory , 7 , 1482 - 1496
15. 15)
  - J. Zhou , L. Cheng , W.F. Bischof . Spatial-temporal modeling of interactive image interpretation. Spat. Vis. , 5 , 455 - 472
16. 16)
  - Zhou, D., Bousquet, O., Lal, T., Weston, J., Schölkopf, B.: `Learning with local and global consistency', Proc. Neural Information Processing Systems, 2003.
17. 17)
  - T. Davis . (2006) Direct methods for sparse linear systems.
18. 18)
  - N. Otsu . A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cyber. , 62 - 66
19. 19)
  - C.C. Chang , C.J. Lin . LIBSVM: a library for support vector machines.
20. 20)
  - R.O. Duda , P.E. Hart , D.G. Stork . Pattern classification.
21. 21)
  - Kumar, M.P., Torr, P.H.S., Zisserman, A.: `Solving markov random fields using second order cone programming relaxations', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2006, p. 1045–1052.
22. 22)
  - Torr, P.H.S.: `Solving markov random fields using semi definite programming', Proc. Int. Workshop on Artificial Intelligence and Statistics, 2003.
23. 23)
  - Leibe, B., Schiele, B.: `Analyzing appearance and contour based methods for object categorization', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2003, p. 409–415.
24. 24)
  - LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: `Gradient-based learning applied to document recognition', Proc. IEEE, 1998, 86, p. 2278–2324.
25. 25)
  - G. Lanckriet , N. Cristianini , P. Bartlett , L. El Ghaoui , M.I. Jordan . Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res. , 27 - 72
26. 26)
  - R.K. Ahuja , T.L. Magnanti , J.B. Orlin . (1993) Network flows: theory, algorithms, and applications.
27. 27)
  - Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: `Total recall: automatic query expansion with a generative feature model for object retrieval', Proc. IEEE Int. Conf. on Computer Vision, 2007, p. 1–8.
28. 28)
  - N. Cristianini , J. Shawe-Taylor . (2000) An introduction to support vector machines.
29. 29)
  - Sinop, A.K., Grady, L.: `A seeded image segmentation framework unifying graph cuts and ramdom walker which yields a new algorithm', Proc. ICCV, 2007.
30. 30)
  - J. Friedman , T. Hastie , R. Tibshirani . Additive logistic regression: a statistical view of boosting. Ann. Stat. , 2 , 337 - 407
31. 31)
  - J. Kittler . On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 226 - 239
32. 32)
  - R. Baeza-Yates , B. Ribeiro-Neto . (1999) Modern information retrieval.
33. 33)
  - Winder, S., Brown, M.: `Learning local image descriptors', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2007.
34. 34)
  - Sengupta, K., Boyer, K.L.: `Using geometric hashing with information theoretic clustering for fast recognition from a large cad modelbase', Proc. IEEE Int. Symp. on Computer Vision, 1995, p. 151–156.
35. 35)
  - Quelhas, P., Monay, F., Odobez, J., Gatica-Perez, D., Tuytelaars, T., Van Gool, L.: `Modelling scenes with local descriptors and latent aspects', Proc. IEEE Int. Conf. on Computer Vision, 2005, I, p. 883–890.
36. 36)
  - Zhu, X., Ghahramani, Z., Lafferty, J.: `Semi-supervised learning using Gaussian fields and harmonic functions', Proc. 20th Int. Conf. on Machine Learning, 2003.
37. 37)
  - Bosch, A., Zisserman, A., Munoz, X.: `Representing shape with a spatial pyramid kernel', Proc. ACM Int. Conf. on Image and Video Retrieval, 2007, p. 401–408.
38. 38)
  - T.K. Ho . The random subspace method for construction decision forests. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 832 - 844
39. 39)
  - Keuchel, J.: `Multiclass image labeling with semidefinite programming', Proc. European Conf. on Computer Vision, 2006, p. 454–467.
40. 40)
  - L. Breiman . Bagging predictors. Mach. Learn. , 2 , 123 - 140
41. 41)
  - Nister, D., Stewenius, H.: `Scalable recognition with a vocabulary tree', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2006, p. 2161–2168.
42. 42)
  - J. Keuchel , C. Schnorr , C. Schellewald , D. Cremers . Binary partitioning, perceptual grouping, and restoration with semidefinite programming. IEEE Trans. Pattern Anal. Mach. Intell. , 11 , 1364 - 1379
43. 43)
  - T. Hastie , R. Tibshirani , J. Friedman . (2009) The elements of statistical learning: data mining, inference and prediction.
44. 44)
  - Cristianini, N., Shawe-Taylor, J., Kandola, J., Elisseeff, A.: `On kernel-target alignment', Proc. Advances in Neural Information Processing Systems, 2002, p. 367–373.
45. 45)
  - Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: `Discovering objects and their location in images', Proc. IEEE Int. Conf. on Computer Vision, 2005, p. 370–377.
46. 46)
  - L. Grady . Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. , 11 , 1768 - 1783
47. 47)
  - Y. Chen , J.Z. Wang , R. Krovetz . Clue: cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Process. , 8 , 1187 - 1201
48. 48)
  - D.G. Lowe . Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis , 2 , 91 - 110
49. 49)
  - S. Lazebnik , C. Schmid , J. Ponce . A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 1265 - 1278
50. 50)
  - Fei-Fei, L., Perona, P.: `A bayesian hierarchical model for learning natural scene categories', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2005, p. 524–531.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

Structured learning approach to image descriptor combination

Structured learning approach to image descriptor combination

Buy article PDF

Buy Knowledge Pack

Thank you

References

Related content