Multi-class Support Vector Machine classifiers using intrinsic and penalty graphs

Pattern Recognition

Volumn 55, Issue , 2016, Pages 231-246

  Iosifidis, Alexandros ^a   Gabbouj, Moncef ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Graph Embedding; Maximum margin classification; Multi class classification; Support Vector Machine

Indexed keywords

CLASSIFIERS; FACE RECOGNITION; GEOMETRY; GESTURE RECOGNITION; IMAGE CLASSIFICATION; LEARNING SYSTEMS; MOTION ESTIMATION; OPTIMIZATION; SUPPORT VECTOR MACHINES;

CLASSIFICATION SCHEME; GRAPH EMBEDDINGS; HUMAN-ACTION RECOGNITION; MAXIMUM MARGIN; MULTI-CLASS CLASSIFICATION; MULTI-CLASS SUPPORT VECTOR MACHINES; OPTIMIZATION PROBLEMS; UNCONSTRAINED ENVIRONMENTS;

CLASSIFICATION (OF INFORMATION);

EID: 84961956410     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.02.002     Document Type: Article

References (54)

1. G. Arvanitidis, A. Tefas, Exploiting graph embedding in support vector machines, in: IEEE International Workshop on Machine Learning for Signal Processing, 2012, pp. 1-6.
2. K. Bache, and M. Lichman UCI Machine Learning Repository 2013 University of California School of Information and Computer Science Irvine, CA < http://archive.ics.uci.edu/ml >
3. 1-norm minimization of the distances between the reduced convex hulls Pattern Recognit. 48 2015 1598 1607
4. C. Chang, and C. Lin LIBSVM: a library for support vector machines ACM Trans. Intell. Syst. Technol. 2 2011 1 27
5. J. Demsar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
6. A. Gaidon, Z. Harchaoui, C. Schmid, Recognizing activities with cluster-tries of tracklets, in: British Machine Vision Conference, 2012, pp. 1-13.
7. A. Gaidon, Z. Harchaoui, and C. Schmid Activity representation with motion hierarchies Int. J. Comput. Vis. 107 2014 219 238
8. S. Hadfield, R. Bowden, Hollywood 3D: recognizing actions in 3D natural scenes, Comput. Vis. Pattern Recognit. (2013) 3398-3405.
9. S. Hadfield, K. Lebeda, R. Bowden, Natural action recognition using invariant 3d motion encoding, in: European Conference on Computer Vision, 2014, 758-771.
10. X. He, Z. Wang, C. Jin, Y. Zheng, and X. Xue A simplified multi-class support vector machine with reduced dual optimization Pattern Recognit. Lett. 33 2012 71 82
11. Y. Huang, Z. Wu, L. Wang, and T. Tan Feature coding in image classification: a comprehensive study IEEE Trans. Pattern Anal. Mach. Intell. 36 2014 493 506
12. A. Iosifidis, A. Tefas, and I. Pitas Discriminant bag of words based representation for human action recognition Pattern Recognit. Lett. 49 2014 185 192
13. A. Iosifidis, A. Tefas, I. Pitas, Human action recognition in stereoscopic videos based on bag of features and disparity pyramids, in: European Signal Processing Conference, 2014, pp. 1317-1321.
14. A. Iosifidis, A. Tefas, I. Pitas, Multi-view regularized extreme learning machine for human action recognition, Artif. Intell.: Methods Appl. (2014) 84-94.
15. A. Iosifidis, A. Tefas, and I. Pitas Class-specific reference discriminant analysis with application in human behavior analysis IEEE Trans. Hum.-Mach. Syst. 45 2015 315 326
16. A. Iosifidis, A. Tefas, and I. Pitas Graph embedded extreme learning machine IEEE Trans. Cybern. 46 2015 311 324
17. M. Jain, H. Jegou, P. Bouthemy, Better exploiting motion for better action recognition, Comput. Vis. Pattern Recognit. (2013) 2555-2562.
18. Y. Jiang, Q. Dai, X. Xue, W. Liu, C. Ngo, Trajectory-based modeling of human actions with motion reference points, in: European Conference on Computer Vision, 2012, pp. 425-438.
19. N. Khan, R. Ksantini, I. Ahmad, and L. Guan Covariance-guided one-class support vector machine Pattern Recognit. 47 2014 2165 2177
20. B. Kijsirijul, N. Ussivakul, Multiclass support vector machines using adaptive directed acyclic graph, in: International Joint Conference on Neural Networks, 2002, pp. 980-985.
21. I. Kotsia, and I. Pitas Facial expression recognition in image sequences using geometric deformation features and support vector machines IEEE Trans. Image Process. 16 2007 172 187
22. I. Kotsia, S. Zafeiriou, and I. Pitas A novel class of multiclass classifiers based on the minimization of within-class-variance IEEE Trans. Neural Netw. 20 2009 14 34
23. I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld, Learning realistic human actions from movies, Comput. Vis. Pattern Recognit. (2008) 1-8.
24. F. Lauer, and Y. Guermeur MSVMpack: a multi-class support vector machine package J. Mach. Learn. Res. 12 2011 2269 2272
25. Y. Li, X. Tian, M. Song, and D. Tao Multi-task proximal support vector machine Pattern Recognit. 48 2015 3249 3257
26. C. Lim, E. Vats, and C. Chan Fuzzy human motion analysis: a review Pattern Recognit. 48 2015 1773 1796
27. M. Luckner, Multiclass svm classification using graphs calibrated by similarity between classes, Knowl.-Based Intell. Inf. Eng. Syst. (2011) 435-444.
28. M. Lyons, S. Akamatsu, M. Kamachi, J. Gyoba, Coding facial expressions with Gabor wavelets, in: IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200-205.
29. M. Marszalek, I. Laptev, C. Schmid, Actions in context, Comput. Vis. Pattern Recognit. (2009) 2929-2936.
30. A. Martinez, and A. Kak An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes Pattern Recognit. 44 2011 1761 1776
31. K. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf An introduction to kernel-based learning algorithms IEEE Trans. Neural Netw. 12 2001 181 201
32. J. Niebles, C. Chend, L. Fei-Fei, Modeling temporal structure of decomposable motion segments for activity classification, in: European Conference on Computer Vision, 2010, pp. 392-405.
33. S. Nikitidis, A. Tefas, and I. Pitas Maximum margin projection subspace learning for visual data analysis IEEE Trans. Image Process. 23 2014 4413 4425
34. D. Oneata, J. Verbeek, C. Schmid, Action and event recognition with fisher vectors on a compact feature set, in: International Conference on Computer Vision, 2013, pp. 1817-1824.
35. G. Orfanidis, A. Tefas, Exploiting subclass information in support vector machines, in: IEEE International Conference on Pattern Recognition, 2012, pp. 1076-1079.
36. S. Peng, Q. Hu, Y. Chen, and J. Dang Improved support vector machine algorithm for heterogeneous data Pattern Recognit. 48 2015 2072 2083
37. F. Samaria, A. Harter, Parameterisation of a stochastic model for human face identification, in: IEEE Workshop on Applied Computer Vision, 1994, pp. 138-142.
38. M. Sapienza, F. CUzzolin, P. Torr, Feature sampling and partitioning for visual vocabulary generation on large action classification datasets, arXiv preprint arXiv:1405.7545 2014, pp. 1-14.
39. B. Scholkpf, and A. Smola Learning with Kernels 2001 MIT Press Cambridge, MA
40. F. Sha, Y. Lin, L. Saul, and D. Lee Multiplicative updates for nonnegative quadratic programming Neural Comput. 19 2007 2004 2031
41. L. Sun, K. Jia, T. Chan, Y. Fang, G. Wang, S. Yan, DLSFA: Deeply-learned slow feature analysis for action recognition, Comput. Vis. Pattern Recognit. (2014) 2625-2632.
42. A. Tefas, C. Kotropoulos, and I. Pitas Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication IEEE Trans. Pattern Anal. Mach. Intell. 23 2001 735 746
43. V. Vapnik Statistical Learning Theory 1998 Wiley New York, NY
44. E. Vig, M. Dorr, D. Cox, Space-variant descriptor sampling for action recognition based on saliency and eye movements, in: European Conference on Computer Vision, 2012, pp. 84-97.
45. N. Vretos, A. Tefas, and I. Pitas Using robust dispersion estimation in support vector machines Pattern Recognit. 46 2013 3441 3451
46. H. Wang, Schmid, Action recognition with improved trajectories, in: International Conference on Computer Vision, 2013, pp. 3551-3558.
47. J. Weston, C. Watkins, Multi-class Support Vector Machines, Technical Report, 1998, pp. 1-10.
48. J. Weston, C. Watkins, Multi-class support vector machines, in: European Symposium on Artificial Neural Networks, 1999, pp. 1-10.
49. S. Yan, D. Xu, B. Zhang, and H. Zhang Graph embedding and extensions: a general framework for dimensionality reduction IEEE Trans. Pattern Anal. Mach. Intell. 29 2007 40 51
50. S. Zafeiriou, A. Tefas, and I. Pitas Minimum class variance support vector machines IEEE Trans. Image Process. 16 2007 2551 2564
51. H. Zhang, L. Cao, and S. Gao A locality correlation preserving support vector machine Pattern Recognit. 47 2014 3168 3178
52. J. Zhang, M. Marszalek, M. Lazebnik, and C. Schmid Local features and kernels for classification of texture and object categories: a comprehensive study Int. J. Comput. Vis. 73 2007 213 238
53. G. Zhong, M. Cheriet, Adapive error-correcting output codes, in: International Joint Conference on Artificial Intelligences, 2013, pp. 1932-1938.
54. G. Zhong, and C. Liu Error-correcting output codes based ensemble feature extraction Pattern Recognit. 46 2013 1091 1100