Object categorization using kernels combining graphs and histograms of gradients

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

Volumn 4142 LNCS, Issue , 2006, Pages 23-34

  Suard, F ^a   Rakotomamonjy, A ^a   Bensrhair, A ^a  

^a University of Rouen   (France)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; FEATURE EXTRACTION; GRADIENT METHODS; GRAPH THEORY; INFORMATION ANALYSIS; MORPHOLOGY; PROBLEM SOLVING;

BINARY MASK; KERNEL PRODUCT; MORPHOLOGICAL SKELETON; OBJECT CATEGORIZATION;

OBJECT RECOGNITION;

EID: 33749680426     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11867661_3     Document Type: Conference Paper

References (18)

1. Francis R. Bach, Gert R. G. Lanckriet, and Michael I. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, page 6, New York, NY, USA, 2004. ACM Press.
2. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992. ACM Press.
3. Horst Bunke and Kim Shearer. A graph distance metric based on the maximal common subgraph. Pattern Recogn. Lett., 19(3-4):255-259, 1998.
4. S. Beucher C. Lantuejoul. On the use of the geodesic metric in image analysis. Journal of miscmcopy, 121(1):39-49, 1981.
5. N. Cristianini and J. Shawe-Taylor. Introduction to Support Vector Machines. Cambridge Univeristy Press, 2000.
6. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Cordelia Schmid, Stefano Soatto, and Carlo Tomasi, editors, International Conference on Computer Vision and Pattern Recognition, volume 2, pages 886-893, INRIA Rhone-Alpes, ZIRST-655, av. de l'Europe, Montbonnot-38334, June 2005.
7. J. Eichhorn and O. Chapelle. Object categorization with svm: kernels for local features. Technical report, MPIK, July 2004.
8. Steven Gold and Anand Rangarajan. A graduated assignment algorithm for graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4):377-388, 1996.
9. T. Darrell K. Grauman. The pyramid match kernel: Discriminative classification with sets of image features. In Proceedings of the IEEE International Conference on Computer Vision, Beijing, China, 2005.
10. H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In Proceedings of the Twentieh International Conference on Machine Learning, 2003.
11. Borgwardt K.M., Ong C.S., Schnauer S., Vishwanathan S.V.N., Smola A.J., and Kriegel H.-P. Protein function prediction via graph kernels. In Intelligent Systems in Molecular Biology, 2005.
12. B. Leibe and B. Schiele. Analyzing appearance and contour based methods for object categorization. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'03), Madison, WI, June 2003.
13. David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
14. C. Di Ruberto. Recognition of shapes by attributed skeletal graphs. Pattern Recognition, 37(1):21-31, 2004.
15. D. Sharvit, J. Chan, H. Tek, and B. Kimia. Symmetry-based indexing of image databases, 1998.
16. A. Shashua, Y. Gdalyahu, and G. Hayon. Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In Proceedings of IEEE Intelligent Vehicles Symposium, 2004.
17. V. Vapnik. Statistical Learning Theory. Wiley, 1998.
18. F. Demirci Y. Keselman, A. Shokoufandeh and S. Dickinson. Many-to-many feature matching using spherical coding of directed graphs. In Proceedings, 8th European Conference on Computer Vision, Prague, Czech Republic, pages 322-335, May 2004.