A novel multiple kernel learning framework for heterogeneous feature fusion and variable selection

IEEE Transactions on Multimedia

Volumn 14, Issue 3 PART1, 2012, Pages 563-574

  Yeh, Yi Ren ^a   Lin, Ting Chu ^a   Chung, Yung Yu ^a,b   Wang, Yu Chiang Frank ^a  

^a RESEARCH CENTER FOR INFORMATION TECHNOLOGY INNOVATION   (Taiwan)

^b Iowa State University of Science and Technology   (United States)

Author keywords

Feature fusion; multiple kernel learning; variable selection

Indexed keywords

COMPACT SETS; DATA SETS; FEATURE DOMAIN; FEATURE FUSION; FEATURE SETS; FEATURE TYPES; HETEROGENEOUS FEATURES; IMAGE DATASETS; KERNEL PARAMETER; MULTIPLE KERNEL LEARNING; OPTIMAL SIZE; PRIOR KNOWLEDGE; RECOGNITION PERFORMANCE; REGULARIZER; VARIABLE SELECTION; VARIABLE SELECTION METHODS; VARIABLE SELECTION PROBLEMS; VARIABLE SPACE;

OPTIMIZATION;

FEATURE EXTRACTION;

EID: 84861148621     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2012.2188783     Document Type: Article

References (34)

1. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
2. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2005, pp. 886-893. (Pubitemid 43897286)
3. P. Gehler and S. Nowozin, "On feature combination for multiclass object classification," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 221-228.
4. T. TZhang,G.Xu, G. Zhu, S. Liu, and H.Lu, "Ageneric framework for event detection in various video domains," in ACM Multimedia, 2010, pp. 103-112.
5. W. Jiang, C. Cotton, S.-F. Chang, D. Ellis, and A. C. Loui, "Audiovisual atoms for generic video concept classification," ACM Trans. Multimedia Comput., Commun., Appl., vol. 6, no. 3, pp. 14:1-14:19, 2010.
6. L. Cao, J. Luo, F. Liang, and T. S. Huang, "Heterogeneous feature machines for visual recognition," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 1095-1102.
7. A. Hanjalic and L.-Q. Xu, "Affective video content representation and modeling," IEEE Trans. Multimedia, vol. 7, no. 1, pp. 143-154, Feb. 2005. (Pubitemid 40178377)
8. G. Irie, T. Satou, A. Kojima, T. Yamasaki, and K. Aizawa, "Affective audio-visual words and latent topic driving model for realizing movie affective scene classification," IEEE Trans. Multimedia, vol. 12, no. 6, pp. 523-535, Oct. 2010.
9. J. Yang, Y. Li, Y. Tian, L. Duan, and W. Gao, "Group-sensitive multiple kernel learning for object categorization," in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 436-443.
10. S.-W. Sun, Y.-C. F.Wang, Y.-L.Hung, C.-L. Chang, S.-S. Chen,H.-M. Wang, and H.-Y. M. Liao, "Automatic annotation of web videos," in Proc. IEEE Int. Conf. Multimedia Expo., 2011, pp. 1-6.
11. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: The MIT Press, 2002.
12. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm," in Proc. Int. Conf. Mach. Learn., 2004, p. 6.
13. A. D. Dileep and C. C. Sekhar, "Representation and feature selection using multiple kernel learning," in Proc. IEEE Int. Joint Conf. Neural Netw., 2009, pp. 717-722.
14. Z. Xu, R. Jin, J. Ye, M. R. Lyu, and I. King, "Non-monotonic feature selection," in Proc. Int. Conf. Mach. Learn., 2009, pp. 1145-1152.
15. A. Kumar and C. Sminchisescu, "Support kernel machines for object recognition," in Proc. IEEE Int. Conf. Comput. Vis., 2007, pp. 1-8.
16. M. Varma and D. Ray, "Learning the discriminative power-invariance trade-off," in Proc. IEEE Int. Conf. Comput. Vis., 2007, pp. 1-8.
17. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
18. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Gr and valet, "SimpleMKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, 2008.
19. M. Kloft, U. Brefeld, P. Laskov, and S. Sonnenburg, "Nonsparse multiple kernel learning," in Proc. NIPS Workshop Kernel Learn.: Automatic Selection of Optimal Kernels, 2008.
20. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf, "Large scale multiple kernel learning," J. Mach. Learn. Res., vol. 7, pp. 1531-1565, 2006. (Pubitemid 44373694)
21. C.-C. Chang and C.-J. Lin, LIBSVM: A Library for Support Vector Machines 2001 [Online].Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm
22. Y.-R. Yeh, Y.-Y. Chung, T.-C. Lin, and Y.-C. F. Wang, "Group lasso regularized multiple kernel learning for heterogeneous feature selection," in Proc. IEEE Int. Joint Conf. Neural Netw., 2011, pp. 103-112.
23. B. S. Manjunath and W. Y. Ma, "Texture features for browsing and retrieval of image data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837-842, Aug. 1996. (Pubitemid 126772825)
24. A. Yanagawa, W. Hsu, and S. F. Chang, "Brief descriptions of visual features for baseline Trecvid concept detectors," Columbia Univ., Tech. Rep., 2006.
25. A. Vailaya, A. Jain, and J. Z. Hong, "On image classification: City vs. l and scape," in Proc. IEEE Workshop Content-Based Access of Image and Video Libraries, 1998, pp. 3-8.
26. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006. (Pubitemid 44811686)
27. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online dictionary learning for sparse coding," in Proc. Int. Conf. Mach. Learn., 2009, pp. 689-696.
28. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2006, pp. 2169-2178. (Pubitemid 44931582)
29. L.-J. Li and L. Fei-Fei, "What, where and who? classifying event by scene and object recognition," in Proc. IEEE Int. Conf. Comput. Vis., 2007, pp. 1-8.
30. J. Wu and J. M. Rehg, "Centrist: A visual descriptor for scene categorization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1489-1501, Aug. 2011.
31. E. Shechtman and M. Irani, "Matching local self-similarities across images and videos," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2007, pp. 1-8.
32. T. Ojala, M. Pietikäinen, and T. Mäenpää, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002. (Pubitemid 34835471)
33. S. Nakariyakul and D. Casasent, "An improvement on floating search algorithms for feature subset selection," Pattern Recognit., vol. 42, no. 9, pp. 1932-1940, 2009.
34. P. Pudil, J. Novovi?ová, and J. Kittler, "Floating searchmethods in feature selection," Pattern Recognit. Lett., vol. 15, no. 11, pp. 1119-1125, 1994.