A new multiple kernel approach for visual concept learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5371 LNCS, Issue , 2009, Pages 250-262

  Yang, Jingjing ^a,b,c   Li, Yuanning ^a,b,c   Tian, Yonghong ^c   Duan, Lingyu ^c   Gao, Wen ^a,b,c  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^c PEKING UNIVERSITY   (China)

Author keywords

Multiple Kernel Learning; Support Vector Machine; Visual Concept Learning

Indexed keywords

CONVEX OPTIMIZATION; IMAGE RETRIEVAL; OPTIMIZATION; SUPPORT VECTOR MACHINES;

CALTECH; CONVEX OPTIMIZATION PROBLEMS; DATA SETS; GLOBAL OPTIMUM; KERNEL SPACES; LEARNING PERFORMANCE; LINEAR COMBINATIONS; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OPTIMAL CLASSIFICATIONS; STATE-OF-THE-ART METHODS; SUPPORT VECTOR MACHINE; TRAINING SAMPLES; VISUAL CONCEPT LEARNING; VISUAL CONCEPTS; VISUAL SIMILARITIES; WIKIPEDIA;

EDUCATION;

EID: 59149106338     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-92892-8_28     Document Type: Conference Paper

References (22)

1. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. PAMI 22(12), 1349-1380 (2000)
2. Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: ICCV Workshop on Content-based Access of Image and Video Databases, Bombay, India, pp. 42-50 (1998)
3. Vogel, J., Schiele, B.: Natural Scene Retrieval Based on a Semantic Modeling Step. In: Proc. Int'l. Conf. Image and Video Retrieval (July 2004)
4. Sivic, J., Russell, B., Efros, A., Zisserman, A.: Discovering Objects and Their Location in Images. In: Proceedings of the IEEE ICCV 2005, pp. 370-377 (2005)
5. Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning Object Categories from Google's Image Search. In: Proceedings of the Tenth ICCV 2005, vol. 2, pp. 1816-1823 (2005)
6. http://www.imageclef.org/2008/wikipedia
7. Fei-Fei, L., Fergus, R., Perona, P.: Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. In: Conference on Computer Vision and Pattern Recognition Workshop (2004)
8. Kumar, A., Sminc, C.: Support Kernel Machines for Object Recognition. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, October 14-21, 2007, pp. 1-8 (2007)
9. Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models. In: Proc. Computer Vision and Pattern Recognition (2005)
10. Fei-Fei, L., Fergus, R., Perona, P.: One-Shot learning of object categories. IEEE Trans. PAMI 28(4), 594-611 (2006)
11. Jia, L., Fei-Fei, L.: What, where and who? Classifying event by scene and object recognition. In: ICCV (2007)
12. Ng, A., Jordan, M.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Advances in NIPS, vol. 12 (2002)
13. Malisiewicz, T., Efros, A.A.: Recognition by Association via Learning Per-exemplar Distances. In: CVPR (June 2008)
14. Torralba, A., Fergus, R., Freeman, W.T.: Tiny images.Technical Report MIT-CSAIL-TR- 2007-024, MIT CSAIL (2007)
15. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
16. Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: ICCV, October 17-21, 2005, vol. 2, pp. 1458-1465 (2005)
17. Lazebnik, S., Schmid, C., Ponce, J.: Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169-2178 (2006)
18. Ling, H., Soatto, S.: Proximity Distribution Kernels for Geometric Context in Category Recognition. In: ICCV, October 14-21, 2007, pp. 1-8 (2007)
19. Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the SMO algorithm. In: NIPS (2004)
20. Sonnenburg, S., Raetsch, G., Schaefer, C., Scholkopf, B.: Large scale multiple kernel learning. Journal of Machine Learning Research, 1531-1565 (2006)
21. Frome, A., Singer, Y., Sha, F., Malik, J.: Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. In: ICCV 2007, pp. 1-8 (2007)
22. Zhang, H., Berg, A.C., Maire, M., Malik, J.: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. In: CVPR. pp. 2126-2136 (2006)