Representing shape with a spatial pyramid kernel

Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007

Volumn , Issue , 2007, Pages 401-408

  Bosch, Anna ^a   Zisserman, Andrew ^b   Munoz, Xavier ^a  

^a UNIVERSITY OF GIRONA   (Spain)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Object and video retrieval; Shape features; Spatial pyramid kernel

Indexed keywords

DATABASE SYSTEMS; INFORMATION RETRIEVAL; LEARNING SYSTEMS;

LEARNING PARAMETERS; OBJECT AND VIDEO RETRIEVAL; SHAPE FEATURES; SPATIAL PYRAMID KERNEL;

SHAPE OPTIMIZATION;

EID: 36849014901     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1282280.1282340     Document Type: Conference Paper

References (27)

1. F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. In Proc. ICML, 2004.
2. A Bissacco, M. H. Yang, and S. Soatto. Detecting humans via their pose. In NIPS, 2006.
3. G. Borgefors. Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE PAMI, 10(6):849-865, 1988.
4. A. Bosch, A. Zisserman, and X. Muoz. Scene classification via pisa. In Proc. ECCV, 2006.
5. C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001. Software available at urlhttp://www.csie.ntu.edu.tw/cjlin/ libsvm.
6. G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV, 2004.
7. N. Dalal and B Triggs. Histogram of oriented gradients for human detection. In Proc. CVPR, 2005.
8. L. Fei-Fei, R. Fergus, and P. Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In IEEE CVPR Workshop of Generative Model Based Vision, 2004.
9. P. F. Felzenszwalb. Learning models for object recognition. In Proc. CVPR, 2001.
10. D. Gavrila and V. Philomin. Real-time object detection for "smart" vehicles. In Proc. ICCV, 1999.
11. K. Grauman and T. Darrell. The pyramid match kernel: Discriminative classification with sets of image features. In Proc. ICCV, 2005.
12. D. Haussler. Convolution kernels on discrete structures. Technical Report UCS-CRL-99-10, 1999.
13. A. K. Jain and K. Karu. Learning texture discrimination masks. IEEE PAMI, 18:195-205, Feb 1996.
14. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In Proc. CVPR, 2006.
15. D. Lewis, T. Jebara, and W. Noble. Nonstationarly kernel combination. In Proc. ICML, 2006.
16. T. Lindeberg and J. Gårding. Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure. In Proc. ECCV, 1994.
17. D. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
18. J. Mutch and D. Lowe. Multiclass object recognition using sparse, localized features. In Proc. CVPR, 2006.
19. A. Opelt, A. Pinz, and A. Zisserman. Incremental learning of object detectors using a visual shape alphabet. In Proc. CVPR, 2006.
20. E. Seemann, B. Leibe, K. Mikolajczyk, and B. Schiele. An evaluation of local shape-based features for pedestrian detection. In Proc. BMVC, 2005.
21. J. Shotton, A. Blake, and R. Cipolloa. Contour-based learning for object detection. In Proc. ICCV, 2005.
22. B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla. Filtering using a tree-based estimator. In ICCV, 2003.
23. M. Szummer and R. W. Picard. Indoor-outdoor image classification. In ICCV Workshop on Content-based Access of Image and Video Databases, 1998.
24. J. Thureson and S. Carlsson. Appearance based qualitative image description for object class recognition. In Proc. ECCV, 2004.
25. G. Wang, Y. Zhang, and L. Fei-Fei. Using dependent regions for object categorization in a generative framework. In Proc. CVPR, 2006.
26. H. Zhang, A. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In Proc. CVPR, 2006.
27. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: a comprehensive study. IJCV, 2007.