A supervised learning framework for generic object detection in images

Proceedings of the IEEE International Conference on Computer Vision

Volumn II, Issue , 2005, Pages 1347-1354

  Ali, Saad ^a   Shah, Mubarak ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA RECORDING; EDUCATION COMPUTING; IMAGE ANALYSIS; IMAGE CODING; IMAGE PROCESSING; LEARNING ALGORITHMS; LIGHTING; PRINCIPAL COMPONENT ANALYSIS;

GENERIC OBJECT DETECTION; KERNEL PRINCIPAL COMPONENT ANALYSIS (KERNEL PCA); NONLINEAR IMAGES; SUPERVISED LEARNING FRAMEWORKS;

OBJECT RECOGNITION;

EID: 33745891802     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.22     Document Type: Conference Paper

References (24)

1. B. Schölkopf et. al., "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, 1998.
2. B. Schölkopf and A. Smola, "Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, "MIT Press, 2002.
3. T. M. Cover, " Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition,"IEEE Transaction on Electronic Computers, 1965.
4. S. Agarwal et. al., "Learning to detect objects in images via a sparse, part-based representation," IEEE PAMI, 26(11), pp. 1475-1490, 2004.
5. H. Schneiderman and T. Kanade, "Object Detection Using the Statistics of Parts," ICCV, 2002.
6. E. Bart et. al., "View-Invariant Recognition Using Corresponding Object Fragments," In Proc. ECCV, 2004.
7. David G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, Vol. 60, no. 2, 2004.
8. K. Mikolajczyk and C. Schmid, "An affine invariant interest point detector, "In Proc. ECCV, 2002.
9. T. Tuytelaars and Luc J. Van Gool, "Content-Based Image Retrieval Based on Local Affinely Invariant Regions," ICVIS, 1999.
10. M.H. Yang et. al., "Face Recognition using Kernel Eigenfaces," International Conference on Image Processing, 2000.
11. B. Moghaddam, "Principal Manifolds and Probabilistic SubSpaces for Visual Recognition," IEEE PAMI, June 2002.
12. M. Vidal-Naquet and S. Ullman, "Object Recognition with Informative Features and Linear Classification, "International Conference of Computer Vision, 2003.
13. R. Fergus et. al., "Object Class Recognition by Unsupervised Scale-Invariant Learning,"CVPR, 2003.
14. A. Opelt et. al., "Weak Hypotheses and Boosting for Generic Object Detection and Recognition,"ECCV, 2004.
15. M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, 1991.
16. S. Romdhani et. al., "A Multi-view Nonlinear Active Shape Model using Kernel PCA," In Proc. BMVC, 1999.
17. D. Cremers et. al., "Nonlinear Shape Statistics via Kernel Spaces," DAGM-Symposium, 2001.
18. C. J. Twining C. J. Taylor, "Kernel Principal Component Analysis and the Construction of Non-Linear Active Shape Models, "In Proc. BMVC, 2001.
19. S. Z. Li et. al., "Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation, "ICCV, 2001.
20. C. Liu, "Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition, "PAMI, 26(5), 2004.
21. Y. Freund and Robert E. Schapire, "Experiments with a New Boosting Algorithm," ICML, 1996.
22. J. Amores et. al., "Boosting contextual information in content-based image retrieval,"ACM SIGMM, 2004.
23. P. Viola and M. Jones, " Fast Multi-View Face Detection, "Technical Report TR2003-96, Mitsubishi Electric Research Laboratories, 2003.
24. K. Levi and Y. Weiss, "Learning Object Detection from a Small Number of Examples: The Importance of Good Featuies,"CVPR, 2004.