Heterogeneous feature machines for visual recognition

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 1095-1102

  Cao, Liangliang ^a   Luo, Jiebo ^b   Liang, Feng ^c   Huang, Thomas S ^a  

^a University of Illinois at Urbana Champaign   (United States)

^b EASTMAN KODAK COMPANY   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION RECOGNITION; CRITICAL ISSUES; DISTANCE METRICS; FAST ALGORITHMS; GRADIENT DESCENT; HETEROGENEOUS FEATURES; KERNEL LOGISTIC REGRESSION; LASSO CONSTRAINT; MODEL COMPLEXITY; OVERFITTING; VISUAL RECOGNITION; WEIGHTING SCHEME;

MACHINERY; REGRESSION ANALYSIS;

COMPUTER VISION;

EID: 77953184874     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459401     Document Type: Conference Paper

References (37)

1. F. Bach. Consistency of the group Lasso and multiple kernel learning. Journal of Machine Learning Research, 9:1179-1225, 2008.
2. F. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. International Conference on Machine Learning, 2004.
3. S. Belongie, J. Malik, and J. Puzicha. Shape context: A new descriptor for shape matching and object recognition. Advances in Neural Information Processing Systems, 2000.
4. C. Blake, and C. Merz. UCI repository of machine learning databases. University of California Irvine, 1998.
5. A. Bobick and J. Davis. The recognition of human movement using temporal templates. IEEE Trans. Pattern Analysis and Machine Intelligence, 2001.
6. A. Bosch, A. Zisserman, and X. Munoz. Representing shape with a spatial pyramid kernel. ACMInternational Conference on Image and Video Retrieval, 2007.
7. A. Bosch, A. Zisserman, and X. Munoz. Image Classification Using ROIs and Multiple Kernel Learning. submitted to International Journal of Computer Vison, 2008.
8. L. Breiman. Random forests. Machine learning, 45(1):5-32, 2001.
9. E. Candes and M. Wakin. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2):21-30, 2008.
10. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. IEEE Conference on Computer Vision and Pattern Recognition, 2:886-893, 2005.
11. A. Efros, A. Berg, G. Mori, and J. Malik. Recognizing action at a distance. IEEE International Conference on Computer Vision, pages 726-733, 2003.
12. E. Frank, M. Hall, L. Trigg, G. Holmes, and I. Witten. Data mining in bioinformatics using Weka, 2004.
13. M Gnen, and E Alpaydin. Localized multiple kernel learning. International Conference on Machine Learning, 2008
14. V. Jain, A. Singhal, and J. Luo. Selective hidden random fields: Exploiting domain specific saliency for event classification. IEEE Conference on Computer Vision and Pattern Recognition, 1, 2008.
15. Y. Ke, R. Sukthankar, and M. Hebert. Event detection in crowded videos. IEEE International Conference on Computer Vision, 2007.
16. C. Kelley. Iterative Methods for Optimization. Society for Industrial Mathematics, 1999.
17. G. Kimeldorf and G. Wahba. Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and Applications, 33:82-95, 1971.
18. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research, 5:27-72, 2004.
19. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. IEEE Conference on Computer Vision and Pattern Recognition, 2008.
20. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. IEEE Conference on Computer Vision and Pattern Recognition, 2006.
21. L.-J. Li and L. Fei-Fei. What, where and who? classifying event by scene and object recognition. IEEE International Conference on Computer Vision, 2007.
22. D. Lowe. Object recognition from local scale-invariant features. Proc. Int'l Conf. Computer Vision, 1999.
23. J. Mairal, et al. Discriminative sparse image models for class-specific edge detection and image interpretation. European Conference on Computer Vision, 2008.
24. J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 22(10):761-767, 2004.
25. L. Meier, S. van de Geer, and P. Buhlmann. The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B, 70(1):53-71, 2008.
26. T. Ojala, M. Pietikäinen, and T.Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 971-987, 2002.
27. A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175, 2001.
28. A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimpleMKL. Journal of Machine Learning Research, 9:2491-2521, 2008.
29. V. Roth. The generalized LASSO. IEEE Transactions on Neural Networks, 15(1), 2004.
30. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. The Journal of Machine Learning Research, 7:1531-1565, 2006.
31. P. Tseng and S. Yun. A coordinate gradient descent method for nonsmooth separable minimization. Mathematical Programming B, 117(1-2), 2009.
32. M. Varma and D. Ray. Learning the discriminative power-invariance trade-off. IEEE International Conference on Computer Vision, 2007.
33. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.
34. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear Spatial Pyramid Matching using Sparse Coding for Image Classification. IEEE Conference on Computer Vision and Pattern Recognition, 2009.
35. H. Yu, M. Li, H. Zhang, and J. Feng. Color texture moments for content-based image retrieval. International Conference on Image Processing, 3:929-932, 2002.
36. M. Yuan and Y. Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B, 68(1):49-67, 2006.
37. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani. 1-norm Support Vector Machines. Advances in Neural Information Processing Systems, 2004.