Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery

Science China Technological Sciences

Volumn 53, Issue 1 SUPPL., 2010, Pages 85-90

  Xiong, Biao ^a   Zhang, XiaoJun ^a   Jiang, WanShou ^a,b  

^a WUHAN UNIVERSITY   (China)

^b STATE KEY LABORATORY OF REMOTE SENSING SCIENCE   (China)

Author keywords

EM algorithms; Gaussian Mixture Model; Image classification; Remote sensing; Semi Supervised Classification

Indexed keywords

IMAGE CLASSIFICATION; LABELED DATA; REMOTE SENSING; SUPPORT VECTOR MACHINES;

CLASSIFICATION INFORMATIONS; EM ALGORITHMS; GAUSSIAN MIXTURE MODEL; LABELED AND UNLABELED DATA; OBJECT ORIENTED CLASSIFICATION; SEMI-SUPERVISED CLASSIFICATION; SEMISUPERVISED CLASSIFICATION (SSC); SUPERVISED CLASSIFICATION;

GAUSSIAN DISTRIBUTION;

EID: 84867563029     PISSN: 16747321     EISSN: 18691900     Source Type: Journal    
DOI: 10.1007/s11431-010-3211-5     Document Type: Article

References (15)

1. Zhu X. Semi-supervised learning literature survey. Technical Report Computer Science. Madison: University of Wisconsin-Madison, 2007
2. Zhu X. Humans Perform Semi-supervised Classification Too. London: AAAI Press, 1999
3. Chapelle O, Schonkopf B, Zien A. Semi-supervised learning. Cambridge: MIT Press, 2006
4. Baluja S. Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data. Adv Neural Inf Proc Syst, 1999. 854-860
5. McCallum A, Nigam K. A comparison of event models for naive bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, 1998
6. Sinha P. Face recognition by humans: Nineteen results all computer vision researchers should know about. Proc IEEE, 2006, 94(11): 1948
7. Bruzzone L, Chi M, Marconcini M. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE T Geosci Remote Sens, 2006, 44(11): 3363
8. Gomez-Chova L. Semi-supervised image classification with Laplacian support vector machines. IEEE Geosci Remote Sens Lett, 2008, 5(3): 336-340
9. Richards J, Jia X. Remote Sensing Digital Image Analysis: An Introduction. Berlin: Springer Verlag, 2006
10. Vatsavai R, Shekhar S, Burk T. A semi-supervised learning method for remote sensing data mining. In: ICTAI, 2005. 207-211
11. Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the International Conference on Machine Learning, 2003. 912-919
12. McCallum A, Nigam K. Employing EM in pool-based active learning for text classification. In: Proceedings of the Fifteenth International Conference on Machine Learning. Morgan Kaufmann, 1998. 350-358
13. Dempster A, Laird N, Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B, 1977, 39(1): 1-38
14. Shahshahani B, Landgrebe D. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE T Geosci Remote, 1994, 32(5): 1087-1095
15. McIver D, Friedl M. Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sens Environ, 2002, 81(2): 253-261