Multiple instance ensemble learning method for high-resolution remote sensing image classification

Yaogan Xuebao/Journal of Remote Sensing

Volumn 17, Issue 1, 2013, Pages 77-97

  Du, Peijun ^a   Samat, Alim ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Classifier; Ensemble learning; Multiple instance learning; Multiple instance learning via embedded instance selection (miles); Uncertainty

Indexed keywords

EID: 84890162039     PISSN: 10074619     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Andrews S, Hofmann T and Tsochantaridis I. 2002.Multiple instance learning with generalized support vector machines. AAAI-02 Proceedings: 943-944
2. Andrews S, Tsochantaridis I and Hofmann T. 2003.Support vector machines for multiple-instance learning. Advances in Neural Information Processing Systems, 15: 561-568
3. Auer P and Ortner R. 2004.A boosting approach to multiple instance learning. MACHINE LEARNING: ECML 2004, Lecture Notes in Computer Science, 3201: 63-74 [DOI: 10.1007/978-3-540-30115-8_9]
4. Beriman L. 1996.Bagging predictors. Machine Learning, 24(2): 123-140 [DOI: 10.1023/A: 1018054314350]
5. Bo Y C and Wang J F. 2005.Combining multiple classifiers for thematic classification of remotely sensed data. Journal of Remote Sensing, 9(5): 555-562
6. Bolton J and Gader P. 2011.Application of multiple-instance learning for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters, 8(5): 889-893 [DOI: 10.1109/LGRS.2011.2135330]
7. Bolton J, Gader P, Hichem F and Torrione P. 2011.Random set framework for multiple instance learning. Information Sciences, 181(11): 2061-2070 [DOI: 10.1016/j.ins.2010.12.020]
8. Chen Y X, Bi J B and Wang J Z. 2006.MILES: multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): 1931-1947 [DOI: 10.1109/TPAMI.2006.248]
9. Chi M M, Qian Q, Benediktsson J A and Rui F. 2009.Ensemble classification algorithm for hyperspectral remote sensing data. IEEE Geoscience and Remote Sensing Letters, 6(4): 762-766 [DOI: 10.1109/LGRS.2009.2024624]
10. Dietterich T G, Lathrop R H and Lozano-Pérez T. 1997.Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1/2): 31-71 [DOI: 10.1016%2fS0004-3702(96) 00034-3]
11. Hansen L K and Salamon P. 1990.Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10): 993-1001 [DOI: 10.1109/34.58871]
12. Hwahwan K and Cha Y K. 2008.A machine learning approach for knowledge base construction incorporating GIS data for land cover classification of landsat ETM+ Image. Journal of the Korean Geographical Society, 43(5): 761-774
13. Kittler J, Hatef M, Duin R P and Matas J. 1998.On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3): 226-239 [DOI: 10.1109/34.667881]
14. Li M, Xue X B and Zhou Z H. 2004.Chinese web index page recommendation based on multi-instance learning. Journal of Software, 15(9): 1328.1335
15. Li J, Cheng Y M, Ge S M and Zeng D. 1996.A method for image retrieval based on salient points feature multiple-instance learning. Journal of Optoelectronics·Laser, 2008, 19(10): 1405-1409
16. Li D X, Peng J Y and He J F. 2010.Image categorization based on EMD-CkNN multi instance learning algorithm. Journal of Optoelectronics·Laser, 21(2): 303-306
17. Li D X, Peng J Y and Li Z. 2007.Object-based image retrieval using semi-supervised MIL algorithm. Control and Decision, 25(7): 981-986
18. Maron O and Lozano-Pérez T. 1998.A framework for multiple- instance learning. Neural Information Processing Systems: 570-675
19. Michelle (US) write, Zeng H J, translated. Machine Learning. 2003. Beijing: Science Press: 14-15
20. Pal M. 2007.Ensemble learning with decision tree for remote sensing classification. World Academy of Science. Engineering and Technology, 36: 258-260
21. Qi Z Q, Xu Y T, Wang L S and Song Y. 2011.Online multiple instance Boosting for object detection. Neurocomputing, 74(10): 1769-1775 [DOI: 10.1016/j.neucom.2011.02.011]
22. Schapire R E. 1990.The strength of weak learnability. Machine Learning, 5(2): 197-227 [DOI: 10.1023/A: 1022648800760]
23. Wang G and Xu X S. 2010.A new multi Instance learning method for scene classification. Journal of Shandong University (Natural Science), 45(7): 108-113
24. Zhang M L. 2010.A k-nearest neighbor based multi-instance multi-label learning algorithm. 22nd International Conference on Tools with Artificial Intelligence, 2: 207-211 [DOI: 10.1109/ICTAI.2010.102]
25. Zhang M L and Zhou Z H. 2004.Improve multi-instance neural networks through feature selection. Neural Processing Letters, 19(1): 1-10 [DOI: 10.1023/B:NEPL.0000016836.03614.9f]
26. Zhou Z H, Sun Y Y and Li Y F. 2009.Multi-Instance learning by treating instances as Non-I. I. D. Samples. Proceedings of the 26th International Conference on Machine Learning (ICML'09). Montreal, Canada: ACM: 1249-1256 [DOI: 10.1145/1553374. 1553534]
27. Zhou Z H and Zhang M L. 2002.Neural networks for multi-instance learning. Technical Report, AI Lab, CS Department, Nanjing University
28. Zhou Z H and Zhang M L. 2003.Ensembles of Multi-Instance Learners. Proceedings of the 14th European Conference on Machine Learning (ECML'03). Cavtat-Dubrovnik, Croatia: 492-502