Online kernel density estimation for interactive learning

Image and Vision Computing - Tập 28 - Trang 1106-1116 - 2010
M. Kristan1,2, D. Skočaj1, A. Leonardis1
1Faculty of Computer and Information Science, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia
2Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia

Tài liệu tham khảo

A. Holub, P. Perona, M. Burl, Entropy-based active learning for object recognition, in: Workshop on Online Learning for Classification, in Conjunction with Conf. Comp. Vis. Pattern Recognition, 2008, pp. 1–8. E.F. Project, CoSy: Cognitive Systems for Cognitive Assistants, 2004–2008. <http://www.cognitivesystems.org>. E.F. Project, CogX: Cognitive Systems that Self-understand and Self-extend, 2008–2012. <http://cogx.eu>. Ardizzone, 1992, Integrating subsymbolic and symbolic processing in artificial vision, J. Intell. Syst., 1, 273 A.M. Arsenio, Developmental learning on a humanoid robot, in: IEEE International Joint Conference On Neural Networks, 2004, pp. 3167–3172. S. Kirstein, H. Wersing, E. Körner, Rapid online learning of objects in a biologically motivated recognition architecture, in: 27th DAGM, 2005, pp. 301–308. Online Learning for Classification Workshop, in Conjunction with IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2007. Online Learning for Classification Workshop, in Conjunction with IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2008. Wand, 1995 Scott, 2001, From kernels to mixtures, Technometrics, 43, 323, 10.1198/004017001316975916 J. Goldberger, S. Roweis, Hierarchical clustering of a mixture model, in: Neural Inf. Proc. Systems, 2005, pp. 505–512. K. Zhang, J.T. Kwok, Simplifying mixture models through function approximation, in: Neural Inf. Proc. Systems, 2006. Mc Lachlan, 1997 Figueiredo, 2002, Unsupervised learning of finite mixture models, IEEE Trans. Pattern Anal. Mach. Intell., 24, 381, 10.1109/34.990138 Zivkovic, 2004, Recursive unsupervised learning of finite mixture models, IEEE Trans. Pattern Anal. Mach. Intell., 26, 651, 10.1109/TPAMI.2004.1273970 A. Corduneanu, C.M. Bishop, Artificial Intelligence and Statistics, Morgan Kaufmann, Los Altos, CA, 2001, Ch. Variational Bayesian Model Selection for Mixture Distributions, pp. 27–34. McGrory, 2007, Variational approximations in Bayesian model selection for finite mixture distributions, Comput. Stat. Data Analysis, 51, 5352, 10.1016/j.csda.2006.07.020 M. Song, H. Wang, Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering, in: SPIE: Intelligent Computing: Theory and Applications, 2005, pp. 174–183. O. Arandjelovic, R. Cipolla, Incremental learning of temporally-coherent Gaussian mixture models, in: British Machine Vision Conference, 2005, pp. 759–768. A. Declercq, J.H. Piater, Online learning of Gaussian mixture models-a two-level approach, in: Intl.l Conf. Comp. Vis., Imaging and Comp. Graph. Theory and Applications, 2008, pp. 605–611. W.F. Szewczyk, Time-evolving Adaptive Mixtures, Tech. Rep., National Security Agency, 2005. Han, 2008, Sequential kernel density approximation and its application to real-time visual tracking, IEEE Trans. Pattern Anal. Mach. Intell., 30, 1186, 10.1109/TPAMI.2007.70771 D. Comaniciu, V. Ramesh, P. Meer, The variable bandwidth mean shift and data-driven scale selection, in: Proc. Int. Conf. Computer Vision, vol. 1, 2001, pp. 438–445. Gales, 2004, Product of Gaussians for speech recognition, Comput. Speech Lang., 20, 22, 10.1016/j.csl.2004.12.002 Girolami, 2003, Probability density estimation from optimally condensed data samples, IEEE Trans. Pattern Anal. Mach. Intell., 25, 1253, 10.1109/TPAMI.2003.1233899 Schölkopf, 2001, Estimating the support of a high-dimensional distribution, Neural Comp., 13, 1443, 10.1162/089976601750264965 Leonardis, 1998, An efficient mdl-based construction of rbf networks, Neural Networks, 11, 963, 10.1016/S0893-6080(98)00051-3 Bischof, 2001, View-based object representations using rbf networks, IVC, 19, 619, 10.1016/S0262-8856(01)00043-9 Pollard, 2002 S. Julier, J. Uhlmann, A General Method for Approximating Nonlinear Transformations of Probability Distributions, Tech. Rep., Department of Engineering Science, University of Oxford, 1996. Jones, 1996, A brief survey of bandwidth selection for density estimation, J. Am. Stat. Assoc., 91, 401, 10.1080/01621459.1996.10476701 Burnham, 2004, Multimodel inference: understanding aic and bic in model selection, Sociologic. Methods Res., 33, 261, 10.1177/0049124104268644 Matlab – The Language of Technical Computing, 2009. <http://www.mathworks.com/products/matlab/>. D. Skočaj, M. Kristan, A. Leonardis, Continuous learning of simple visual concepts using incremental kernel density estimation, in: International Conference on Computer Vision Theory and Applications, 2008. Press, 1992, Numerical recipes in C E. Veach, L.J. Guibas, Optimally combining sampling techniques for monte carlo rendering, in: Computer Graphics and Interactive Techniques, 1995, pp. 419–428.