Gaussian processes for object categorization

International Journal of Computer Vision

Volumn 88, Issue 2, 2010, Pages 169-188

  Kapoor, Ashish ^a   Grauman, Kristen ^b   Urtasun, Raquel ^c   Darrell, Trevor ^c  

^a MICROSOFT RESEARCH   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Active learning; Gaussian process; Kernel combination; Object recognition

Indexed keywords

ACTIVE LEARNING; CLASS LABELS; CLASSIFICATION PERFORMANCE; COVARIANCE FUNCTION; DISCRIMINATIVE METHODS; GAUSSIAN PROCESS; GAUSSIAN PROCESSES; HYPERPARAMETERS; INTERACTIVE LABELING; NON-PROBABILISTIC; OBJECT CATEGORIZATION; OBJECT CATEGORY RECOGNITION; OPTIMAL COMBINATION; PREDICTIVE MODELS; PROBABILISTIC FORMULATION; REGRESSION TECHNIQUES; TEST POINTS; TRAINING DATA; UNCERTAINTY MODELS; VISUAL OBJECTS;

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LABELS; UNCERTAINTY ANALYSIS;

OBJECT RECOGNITION;

EID: 77951294698     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-009-0268-3     Document Type: Article

References (56)

1. Abramson, Y., & Freund, Y. (2004). Active learning for visual object recognition (Technical report). UCSD.
2. Belongie, S., Malik, J., & Puzicha, J. (2001). Matching shapes. In ICCV.
3. Berg, A., & Malik, J. (2001). Geometric blur for template matching. In CVPR.
4. Boiman, O., Shechtman, E., & Irani, M. (2008). In defense of nearest-neighbor based image classification. In CVPR.
5. Bosch, A., Zisserman, A., & Muñoz, X. (2007). Representing shape with a spatial pyramid kernel. In CIVR.
6. Chang, C., & Lin, C. (2001). LIBSVM: a library for SVMs.
7. Chang, E. Y., Tong, S., Goh, K., & Chang, C. (2005). Support vector machine concept-dependent active learning for image retrieval. IEEE Transactions on Multimedia.
8. Chum, O., & Zisserman, A. (2007). An exemplar model for learning object classes. In Proceedings of the IEEE conference on computer vision and pattern recognition.
9. Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13(1).
10. Fei-Fei, L., Fergus, R., & Perona, P. (2006). One-shot learning of object categories. IEEE Transaction on Pattern Recognition and Machine Intelligence.
11. Fergus, R., Perona, P., & Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. In CVPR.
12. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3).
13. Frome, A., Singer, Y., Sha, F., & Malik, J. (2007). Learning globally-consistent local distance functions for shape-based image retrieval and classification. In ICCV.
14. Grauman, K., & Darrell, T. (2005). The pyramid match kernel: Discriminative classification with sets of image features. In ICCV.
15. Grauman, K., & Darrell, T. (2006a). Approximate correspondences in high dimensions. In NIPS.
16. Grauman, K., & Darrell, T. (2006b). Unsupervised learning of categories from sets of partially matching image features. In CVPR.
17. Kadir, T., & Brady, M. (2003). Scale saliency: A novel approach to salient feature and scale selection. In International conference visual information engineering.
18. Kapoor, A., Grauman, K., Urtasun, R., & Darrell, T. (2007). Active learning with Gaussian processes for object categorization. In ICCV.
19. Kim, H. C., Kim, D., Ghahramani, Z., & Bang, S. Y. (2006). Appearance-based gender classification with Gaussian processes. Pattern Recognition Letters.
20. Krause, A., Singh, A., & Guestrin, C. (2008). Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. In JMLR.
21. Kumar, A., & Sminchisescu, C. (2007). Support kernel machines for object recognition. In ICCV.
22. Lawrence, N. (2004). Gaussian process latent variable models for visualisation of high dimensional data. In NIPS.
23. Lawrence, N., Seeger, M., & Herbrich, R. (2002). Fast sparse Gaussian process method: Informative vector machines. In NIPS.
24. Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR.
25. Lin, Y. Y., Liu, T. Y., & Fuh, C. S. (2007). Local ensemble kernel learning for object category recognition. In CVPR.
26. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. IJCV, 60(2).
27. MacKay, D. (1992) Information-based objective functions for active data selection. Neural Computation, 4(4).
28. McCallum, A. K., & Nigam, K. (1998). Employing EM in pool-based active learning for text classification. In ICML.
29. Mikolajczyk, K., & Schmid, C. (2001). Indexing based on scale invariant interest points. In ICCV.
30. K. Mikolajczyk C. Schmid 2004 Scale and affine invariant interest point detectors IJCV 1 60 63 86 10.1023/B:VISI.0000027790.02288.f2
31. Minka, T. P. (2001). A family of algorithms for approximate Bayesian inference. PhD thesis, MIT.
32. Moosmann, B. T. F., & Jurie, F. (2007). Fast discriminative visual codebooks using randomized clustering forests. In NIPS.
33. Muslea, I., Minton, S., & Knoblock, C. A. (2002). Active + semi-supervised learning = robust multi-view learning. In ICML.
34. Nister, D., & Stewenius, H. (2006). Scalable recognition with a vocabulary tree. In CVPR.
35. Nowak, E., Jurie, F., & Triggs, B. (2006). Sampling strategies for bag-of-features image classification. In ECCV.
36. Rasmusen, C. E., & Williams, C. (2006). Gaussian processes for machine learning. Cambridge: MIT Press.
37. Seeger, M. (2004). Gaussian processes for machine learning. International Journal of Neural Systems, 14(2).
38. Shen, Y., Ng, A., & Seeger, M. (2006). Fast Gaussian process regression using kd-trees. In NIPS.
39. Sivic, J., & Zisserman, A. (2003). Video Google: a text retrieval approach to object matching in videos. In ICCV.
40. Sivic, J., Russell, B., Efros, A., Zisserman, A., & Freeman, W. (2005). Discovering object categories in image collections. In ICCV.
41. Snelson, E., & Ghahramani, Z. (2006). Sparse Gaussian processes using pseudo-inputs. In NIPS.
42. Sudderth, E., Torralba, A., Freeman, W., & Willsky, A. (2005). Describing visual scenes using transformed Dirichlet processes. In NIPS.
43. Tong, S., & Koller, D. (2000). Support vector machine active learning with applications to text classification. In ICML.
44. Tresp, V. (2000). Mixtures of Gaussian processes. In NIPS.
45. Tsang, I. W.-H., & Kwok, J. T.-Y. (2006). Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks.
46. Urtasun, R., & Darrell, T. (2008). Local probabilistic regression for activity-independent human pose inference. In CVPR.
47. Urtasun, R., Fleet, D. J., Hertzman, A., & Fua, P. (2005). Priors for people tracking from small training sets. In ICCV.
48. Urtasun, R., Fleet, D. J., & Fua, P. (2006). Gaussian process dynamical models for 3d people tracking. In CVPR.
49. Varma, M., & Ray, D. (2007). Learning the discriminative power-invariance trade-off. In ICCV.
50. von Ahn, L., & Dabbish, L. (2004). Labeling images with a computer game. In ACM CHI.
51. von Ahn, L., Liu, R., & Blum, M. (2006). Peekaboom: A game for locating objects in images. In ACM CHI.
52. Wallraven, C., Caputo, B., & Graf, A. (2003). Recognition with local features: the kernel recipe. In ICCV.
53. C. Williams D. Barber 1998 Bayesian classification with Gaussian processes IEEE Transaction on Pattern Recognition and Machine Intelligence 20 12 1342 1351 10.1109/34.735807
54. Williams, O. (2006). A switched Gaussian process for estimating disparity and segmentation in binocular stereo. In NIPS.
55. Zhang, H., Berg, A., Maire, M., & Malik, J. (2006). SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In CVPR.
56. Zhu, X., Lafferty, J., & Ghahramani, Z. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In Workshop on the continuum from labeled to unlabeled data in machine learning and data mining at ICML.