3DString: A feature string kernel for 3D object classification on voxelized data

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2006, Pages 198-207

  Afalg, Johannes ^a   Borgwardt, Karsten M ^a   Kriegel, Hans Peter ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

3D object classification; String kernel

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; FEATURE EXTRACTION; INFORMATION MANAGEMENT; MEDICINE; THREE DIMENSIONAL;

OBJECT CLASSIFICATION; STRING KERNEL; VOXEL REPRESENTATIONS;

OBJECT RECOGNITION;

EID: 34547633088     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1183614.1183647     Document Type: Conference Paper

References (32)

1. M. Ankerst, G. Kastenmüller, H.-P. Kriegel, and T. Seidl. 3D Shape Histograms for Similarity Search and Classification in Spatial Databases. In Proceedings of the 6th International Symposium on Spatial Databases, pages 207-226, 1999.
2. A. Ben-Hur, D. Horn, H. Siegelmann, and V. Vapnik. A support vector method for hierarchical clustering. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 367-373. MIT Press, 2001.
3. H. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. Bhat, H. Weissig, I. Shindyalov, and P. Bourne. The protein data bank. Nucleic Acids Research, 28:235-242, 2000.
4. C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
5. D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung. On Visual Similarity Based 3D Model Retrieval. In Computer Graphics Forum, volume 22, pages 223-232, 2003.
6. P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In VLDB '97: Proceedings of the 23rd International Conference on Very Large Data Bases, pages 426-435, 1997.
7. H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik. Support vector regression machines. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 155-161, Cambridge, MA, 1997. MIT Press.
8. T. Funkhouser, P. Min, M. Kazhdan, J. Chen, A. Halderman, D. Dobkin, and D. Jacobs. A search engine for 3d models. In ACM Transactions on Graphics, pages 83-105, 2003.
9. D. Haussler. Convolutional kernels on discrete structures. Technical Report UCSC-CRL-99 -10, Computer Science Department, UC Santa Cruz, 1999.
10. T. S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 487-493. MIT Press, 1999.
11. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - -Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
12. I. Jolliffe. Principal Component Analysis. Springer, 1986.
13. A. Kaufman. An Algorithm for 3D Scan-Conversion of Polygons. In Proc. Eurographics, pages 197-208, 1987.
14. M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz. Rotation invariant spherical harmonic representation of 3d shape descriptors. In Symposium on Geometry Processing, pages 167-175, 2003.
15. H.-P. Kriegel, P. Kroger, Z. Mashael, M. Pfeifle, M. Pötke, and T. Seidl. Effective Similarity Search on Voxelized CAD Objects. In Proc. 8th Int. Conf. on Database Systems for Advanced Applications, Kyoto, Japan, pages 27-36, 2003.
16. C. Leslie, E. Eskin, and W. S. Noble. The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing, pages 564-575, 2002.
17. C. Leslie, E. Eskin, J. Weston, and W. S. Noble. Mismatch string kernels for SVM protein classification. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, volume 15, Cambridge, MA, 2002. MIT Press.
18. L. Liao and W. S. Noble. Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In RECOMB, pages 225-232, 2002.
19. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.-R. Müller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors, Neural Networks for Signal Processing IX, pages 41-48. IEEE, 1999.
20. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. Matching 3D Models with Shape Distributions. In International Conference on Shape Modeling and Applications, pages 154-166, 2001.
21. G. Rätsch, S. Sonnenburg, and B. Schölkopf. Rase: recognition of alternatively spliced exons in c.elegans. Bioinformatics, 21 Suppl 1:i369-i377, Jun 2005.
22. B. Schölkopf. Support Vector Learning. R. Oldenbourg Verlag, Munich, 1997. Download: http://www.kernel-machines.org.
23. B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
24. B. Schölkopf, A. J. Smola, and K.-R. Müller. Kernel principal component analysis. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - -Support Vector Learning, pages 327-352. MIT Press, Cambridge, MA, 1999.
25. J.-L. Shih, C-H. Lee, and J. Wang. 3D object retrieval system based on grid D2. Electronics Letters, 41(4), 2005.
26. P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser. The Princeton Shape Benchmark. In Shape Modeling International, Genova, Italy, 2004.
27. S. Sonnenburg, G. Ratsch, and B. Schölkopf. Large scale genomic sequence svm classifiers. In International Conference on Machine Learning, 2005.
28. S. Sonnenburg, G. Ratsch, and B. Schölkopf. A General and Efficient Multiple Kernel Learning Algorithm. In Neural Information Processing Systems, 2005.
29. V. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.
30. S. V. N. Vishwanathan and A. J. Smola. Fast kernels for string and tree matching. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 569-576. MIT Press, Cambridge, MA, 2003.
31. C Watkins. Dynamic alignment kernels. CSD-TR-98-11, Royal Holloway, University of London, Egham, Surrey, UK, 1999.
32. A. Zien, G. Rätsch, S. Mika, B. Schölkopf, T. Lengauer, and K.-R. Müller. Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. Bioinformatics, 16(9):799-807, Sept. 2000.