The influence of hubness on nearest-neighbor methods in object recognition

Proceedings - 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, ICCP 2011

Volumn , Issue , 2011, Pages 367-374

  Tomašev, Nenad ^a   Brehar, Raluca ^b   Mladenić, Dunja ^a   Nedevschi, Sergiu ^b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b TECHNICAL UNIVERSITY OF CLUJ NAPOCA   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC IMAGE PROCESSING; DATA POINTS; ESSENTIAL PROBLEMS; FEATURE REPRESENTATION; HIGH DIMENSIONAL DATA; IMAGE DATA; IMAGE DATASETS; NEAREST NEIGHBOR CLASSIFICATION; NEAREST NEIGHBOR METHOD; RECOGNITION ACCURACY;

CLUSTERING ALGORITHMS;

OBJECT RECOGNITION;

EID: 80755189587     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCP.2011.6047899     Document Type: Conference Paper

References (40)

1. C. C. Aggarwal, A. Hinneburg, and D. A. Keim, "On the surprising behavior of distance metrics in high dimensional spaces," in Proc. 8th Int. Conf. on Database Theory (ICDT), 2001, pp. 420-434.
2. J. Aucouturier, "Ten experiments on the modelling of polyphonic timbre," Docteral dissertation, University of Paris 6, Tech. Rep., 2006.
3. J. Aucouturier and F. Pachet, "Improving timbre similarity: How high is the sky?" Journal of Negative Results in Speech and Audio Sciences, vol. 1, 2004.
4. O. Boiman, E. Shechtman, and M. Irani, "In defense of nearest-neighbor based image classification," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2008.
5. C.J.Stone, "Consistent nonparametric regression," Annals of Statistics, vol. 5, pp. 595-645, 1977.
6. L. Devroye, "On the inequality of cover and hart," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, pp. 75-78, 1981.
7. M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: Survey and experiments," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2179-2195, 2009.
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," 2004.
9. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part based models," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, 2010.
10. E. Fix and J. Hodges, "Discriminatory analysis, nonparametric discrimination: consistency properties," USAF School of Aviation Medicine, Randolph Field, Texas, Tech. Rep., 1951.
11. D. François, V. Wertz, and M. Verleysen, "The concentration of fractional distances," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 7, pp. 873-886, 2007.
12. S. Gu, Y. Zheng, and C. Tomasi, "Efficient visual object tracking with online nearest neighbor classifier," in Proceedings of the 10th Asian conference on Computer vision - Volume Part I, ser. ACCV'10. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 271-282.
13. E.-H. Han, G. Karypis, and V. Kumar, "Text categorization using weight adjusted k-nearest neighbor classification," in Advances in Knowledge Discovery and Data Mining, ser. Lecture Notes in Computer Science, D. Cheung, G. Williams, and Q. Li, Eds. Springer Berlin / Heidelberg, 2001, vol. 2035, pp. 53-65. (Pubitemid 33255095)
14. M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek, "Can shared-neighbor distances defeat the curse of dimensionality?"in Proceedings of the 22nd international conference on Scientific and statistical database management, ser. SSDBM'10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 482-500. [Online]. Available: http://portal.acm.org/citation.cfm?id=1876037. 1876078
15. R. Jensen and C. Cornelis, "A new approach to fuzzy-rough nearest neighbour classification," in Rough Sets and Current Trends in Computing, ser. Lecture Notes in Computer Science, C.-C. Chan, J. Grzymala- Busse, and W. Ziarko, Eds. Springer Berlin / Heidelberg, 2008, vol. 5306, pp. 310-319.
16. M. Jones and P. Viola, "Rapid object detection using a boosted cascade of simple features," in Conf. Computer Vision and Pattern Recognition, 2001.
17. J. E. Keller, M. R. Gray, and J. A. Givens, "A fuzzy k-nearest-neighbor algorithm," in IEEE Transactions on Systems, Man and Cybernetics, 1985, pp. 580-585.
18. A. K. L. Devroye, L. Gyorfi and G. Lugosi, "On the strong universal consistency of nearest neighbor regression function estimates," Annals of Statistics, vol. 22, pp. 1371-1385, 1994.
19. F.-F. Li and P. Perona, "A bayesian hierarchical model for learning natural scene categories," in Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), ser. CVPR '05, vol. 2. Washington, DC, USA: IEEE Computer Society, 2005, pp. 524-531. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2005.16
20. R. Lienhart and J. Maydt, "An extended set of haar-like features for rapid object detection," in IEEE ICIP 2002, 2002, pp. 900-903.
21. D. Lowe, "Object recognition from local scale-invariant features," in ICCV, 1999, pp. 1150-1157.
22. -, "Object recognition from local scale-invariant features," in The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, 1999, pp. 1150-1157.
23. D. G. Lowe, "Distinctive image features from scale-invariant keypoints,"International Journal of Computer Vision, vol. 60, no. 2, p. 91, Nov. 2004.
24. M. R. Min, D. A. Stanley, Z. Yuan, A. J. Bonner, and Z. Zhang, "A deep non-linear feature mapping for large-margin knn classification," in ICDM, 2009, pp. 357-366.
25. G. M. Overett, L. Petersson, N. Brewer, N. Pettersson, and L. Andersson, "A new pedestrian dataset for supervised learning," in IEEE Intelligent Vehivles Symposium, Eindhoven, The Netherlands, June 2008.
26. C. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection," in International Conference on Computer Vision, 1998.
27. M. Radovanovíc, A. Nanopoulos, and M. Ivanovíc, "Nearest neighbors in high-dimensional data: The emergence and influence of hubs," in Proc. 26th Int. Conf. on Machine Learning (ICML), 2009, pp. 865-872.
28. -, "On the existence of obstinate results in vector space models," in Proc. 33rd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2010, pp. 186-193.
29. -, "Hubs in space: Popular nearest neighbors in high-dimensional data," Journal of Machine Learning Research, vol. 11, pp. 2487-2531, 2011.
30. W. Shang, H. Huang, H. Zhu, Y. Lin, Y. Qu, and H. Dong, "An adaptive fuzzy knn text classifier," in Computational Science - ICCS 2006, ser. Lecture Notes in Computer Science, V. Alexandrov, G. van Albada, P. Sloot, and J. Dongarra, Eds. Springer Berlin / Heidelberg, 2006, vol. 3993, pp. 216-223. (Pubitemid 44152358)
31. Y. Song, J. Huang, D. Zhou, H. Zha, and C. L. Giles, "Iknn: Informative k-nearest neighbor pattern classification," in Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases, ser. PKDD 2007. Berlin, Heidelberg: Springer-Verlag, 2007, pp. 248-264.
32. T.M.Cover and P.E.Hart, "Nearest neighbor pattern classification," IEEE Transactions on Information Theory, vol. IT-13, no. 1, pp. 21-27, 1967.
33. N. Tomašev and D. Mladeníc, "Nearest neighbor voting in high dimensional data: Learning from past occurrences," Artificial Intelligence Laboratory, Jozef Stefan Institute, Tech. Rep., 2011.
34. N. Tomašev, M. Radovanovíc, D. Mladeníc, and M. Ivanovíc, "Hubnessbased fuzzy measures for high dimensional k-nearest neighbor classification," in MLDM, 2011.
35. -, "The role of hubness in clustering high-dimensional data," in PAKDD, 2011.
36. B. Triggs and N. Dalal, "Histogram of oriented gradients for human detection," in IEEE Computer Vision and Pattern Recognition, 2005.
37. J. Wang, P. Neskovic, and L. N. Cooper, "Improving nearest neighbor rule with a simple adaptive distance measure," Pattern Recogn. Lett., vol. 28, pp. 207-213, January 2007. (Pubitemid 44716403)
38. S. Wang, Q. Huang, S. Jiang, and Q. Tian, "Nearest-neighbor classification using unlabeled data for real world image application," in Proceedings of the international conference on Multimedia, ser. MM '10. New York, NY, USA: ACM, 2010, pp. 1151-1154.
39. K. Q. Weinberger, J. Blitzer, and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification," in In NIPS. MIT Press, 2006.
40. Z. Zhang and R. Zhang, Multimedia Data Mining: a Systematic Introduction to Concepts and Theory. Chapman and Hall, 2008.