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Articles

A new multimedia classification approach: Bayesian of inductive cognition algorithm based on Dirichlet process

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Pages 331-339 | Published online: 18 Jul 2013

REFERENCES

  • Song, D. J. and Tao, D. C. Biologically inspired feature manifold for scene classification. IEEE Trans. Image Process., 2010, 19, 174–184.
  • Brezeale, D. and Cook, D. J. Automatic video classification: a survey of the literature. IEEE Trans. Syst. Man Cybern. C, 2008, 38C, 416–433.
  • Bosch, A., Zisserman, A. and Munoz, X. Scene classification using a hybrid generative/discriminative approach. IEEE Trans. Patt. Anal. Mach. Intell., 2008, 30, 712–727.
  • Gao, Y., Wang, W.-B. and Yong, J.-H. A video summarization tool using two-level redundancy detection for personal video recorders. IEEE Trans. Consum. Electron., 2008, 54, 521–526.
  • Weiss, Y. and Adelson, E. H. A unified mixture framework for motion segmentation: Incorporating spatial coherence and estimating the number of models, Proc. IEEE Computer Society Conf. on Computer vision and pattern recognition: CVPR ’96, San Francisco, CA, USA, June 1996, IEEE Computer Society, pp. 321–326.
  • Bregler, C. Learning and recognizing human dynamics in video sequences, Proc. IEEE Computer Society Conf. on Computer vision and pattern recognition: CVPR ’97, San Juan, Puerto Rico, June 1997, IEEE Computer Society, pp. 568–574.
  • Huang, J. C., Liu, Z. and Wang, Y. Joint scene classification and segmentation based on hidden Markov model. IEEE Trans. Multimedia, 2005, 7, 538–550.
  • Khan, S. and Shah, M. Object based segmentation of video, using color, motion and spatial information, Proc. IEEE Computer Society Conf. on Computer vision and pattern recognition: CVPR 2001, IEEE Computer Society, Vol. 2, pp. 746–751.
  • Chen, D.-Y., Cannons, K., Tyan, H.-R., Shih, S.-W. and Mark Liao, H.-Y. Spatiotemporal motion analysis for the detection and classification of moving targets. IEEE Trans. Multimedia, 2008, 10, 1578–1591.
  • Goldberger, J. and Greenspan, H. Context-based segmentation of image sequences. IEEE Trans. Patt. Anal. Mach. Intell., 2006, 28, 463–468.
  • Bernardo J. M. and Smith A. F. M. Bayesian Theory, 1994 (Wiley, Chichester).
  • Duan, L.-Y., Jin, J. S., Tian, Q. and Xu, C.-S. Nonparametric motion characterization for robust classification of camera motion patterns. IEEE Trans. Multimedia, 2006, 8, 323–340.
  • Ferguson T. S. A Bayesian analysis of some nonpara- metric problems. Ann. Stat., 1973, 1, 209–230.
  • Bouguila, N. and Ziou, D. A Dirichlet process mixture of Dirichlet distributions for classification and prediction, Proc. IEEE Workshop on Machine learning for signal processing: MLSP 2008, Cancún, Mexico, October 2008, IEEE, pp. 297–302.
  • Cao, L.-L., Luo, J. B., Kautz, H. and Huang, T. S. Image annotation within the context of personal photo collections using. IEEE Trans. Multimedia, 2009, 11, 208–219.
  • Neal, R. M. Markov chain sampling methods for Dirichlet process mixture models. J. Comput. Graph. Stat., 2000, 9, 249–265.
  • Caron, F., Davy, M., Doucet, A., Duflos, E. and Vanheeghe, P. Bayesian inference for linear dynamic models with Dirichlet process mixtures. IEEE Trans. Signal Process., 2008, 56, 71–84.
  • Chipman, H., George, E. and McCulloch, R. Bayesian cart model search with discussion. J. Am. Stat. Assoc., 1998, 93, 935–960.
  • Denison, D., Holmes, C., Mallick, B. and Smith, A. Bayesian Methods for Nonlinear Classification and Regression, 2002 (Wiley, West Sussex).
  • Wang, X. G., Ma, X. X. and Grimson, W. E. L. Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Patt. Anal. Mach. Intell., 2009, 31, 539–555.
  • Stolcke, A. and Omohundro, S. Hidden Markov model induction by Bayesian model merging. Adv. Neural Inform. Process. Syst., 1993, 5, 11–18.
  • Nikseresht, A. and Gelgon, M. Gossip-based computation of a Gaussian mixture model for distributed multimedia indexing. IEEE Trans. Multimedia, 2008, 10, 385–392.
  • Williams, C. A MCMC approach to hierarchical mixture modeling. Adv. Neural Inform. Process. Syst., 2000, 12, 680–686.
  • Neal, R. M. Density modeling and clustering using Dirichlet diffusion trees. Bayesian Stat., 2003, 7, 619–629.
  • Jensen, C., S., Lin, D. and Ooi, B. C. Continuous clustering of moving objects. IEEE Trans. Knowl. Data Eng., 2007, 19, 1161–1173.
  • Banfield, J. D. and Raftery, A. E. Model-based Gaussian and non-Gaussian clustering. Biometrics, 1993, 49, 803–821.
  • Vaithyanathan S. and Dom, B. Model-based hierarchical clustering, Proc. 16th Conf. on Uncertainty in artificial intelligence, Stanford, CA, USA, June 2000, Stanford University, pp. 599–608.
  • Iwayama, M. and Tokunaga, T. Hierarchical Bayesian clustering for automatic text classification, Proc. 14th Int. Joint Conf. on Artificial intelligence: IJCAI-95, Montreal, Que., Canada, August 1995, CSCSI, pp. 1322–1327.
  • Zhang, Z. F., Masseglia, F., Jain, R. and Bimbo, A. D. Editorial: Introduction to the special issue on multimedia data mining. IEEE Trans. Multimedia, 2008, 10, 165–166.
  • Ekin, A., Tekalp, A. M. and Mehrotra, R. Automatic soccer video analysis and summarization. IEEE Trans. Image Process., 2003, 12, 796–807.
  • Jin, L. C., Wan, W. G., Cui, B., Yu, X. Q. and Xu, H. W. A new multimedia information data mining method, Proc. ACM 2009 World Summit on Genetic and evolutionary computation, Shanghai, China, June 2009, ACM, pp. 899–902.
  • Duan, L.-Y., Xu, M., Tian, Q., Xu, C.-S. and Jin, J. S. A unified framework for semantic shot classification in sports video. IEEE Trans. Multimedia, 2005, 7, 1066–1083.

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