Superpixel-based interactive classification of very high resolution images

Brazilian Symposium of Computer Graphic and Image Processing

Volumn , Issue , 2014, Pages 173-179

  Vargas, John E ^a   Saito, Priscila T M ^a,b   Falcão, Alexandre X ^a   De Rezende, Pedro J ^a   Dos Santos, Jefersson A ^c  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

^c FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

active learning; dataset reduction; superpixel representation; supervised classification; Very high resolution image processing

Indexed keywords

IMAGE CLASSIFICATION; LARGE DATASET; LEARNING SYSTEMS; SUPERPIXELS;

ACTIVE LEARNING; ACTIVE LEARNING STRATEGIES; ACTIVE-LEARNING PROCESS; IMAGE INFORMATION; INTERACTIVE CLASSIFICATION; SUPERVISED CLASSIFICATION; SUPERVISED TRAININGS; VERY HIGH RESOLUTION (VHR) IMAGE;

CLASSIFICATION (OF INFORMATION);

EID: 84907969624     PISSN: 15301834     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SIBGRAPI.2014.49     Document Type: Conference Paper

References (31)

1. D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, "A survey of active learning algorithms for supervised remote sensing image classification," IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 3, pp. 606-617, June 2011
2. B. Demir, C. Persello, and L. Bruzzone, "Batch-mode active-learning methods for the interactive classification of remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 3, pp. 1014-1031, March 2011
3. S. Rajan, J. Ghosh, and M. Crawford, "An active learning approach to hyperspectral data classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1231-1242, April 2008
4. D. Tuia, E. Pasolli, andW. Emery, "Using active learning to adapt remote sensing image classifiers," Remote Sensing of Environment, vol. 115, no. 9, pp. 2232-2242, 2011
5. J. A. dos Santos, P.-H. Gosselin, S. Philipp-Foliguet, R. Da S Torres, and A. X. Falc?ao, "Interactive multiscale classification of high-resolution remote sensing images," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 4, pp. 2020-2034, Aug 2013
6. M. Li and I. Sethi, "Confidence-based active learning," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1251-1261, Aug 2006
7. K. Brinker, "Incorporating diversity in active learning with support vector machines," in Proceedings of the Twentieth International Conference on Machine Learning, 2003
8. P. T. M. Saito, P. J. de Rezende, A. X. Falc?ao, C. T. N. Suzuki, and J. F. Gomes, "Improving active learning with sharp data reduction," in WSCG International Conference on Computer Graphics, Visualization and Computer Vision, 2012, pp. 27-34
9. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "Slic superpixels compared to state-of-the-art superpixel methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2274-2282, Nov 2012
10. P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph-based image segmentation," International Journal of Computer Vision, vol. 59, no. 2, pp. 167-181, Sep. 2004
11. A. Moore, S. Prince, J. Warrell, U. Mohammed, and G. Jones, "Superpixel lattices," in IEEE Conference on Computer Vision and Pattern Recognition, June 2008, pp. 1-8
12. A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, "Turbopixels: Fast superpixels using geometric flows," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2290-2297, Dec. 2009
13. O. Veksler, Y. Boykov, and P. Mehrani, "Superpixels and supervoxels in an energy optimization framework," in European Conference on Computer Vision, 2010
14. L. M. Rocha, F. A. M. Cappabianco, and A. X. Falc?ao, "Data clustering as an optimum-path forest problem with applications in image analysis," Int. J. Imaging Syst. Technol., vol. 19, no. 2, pp. 50-68, Jun. 2009
15. P. T. M. Saito, P. J. de Rezende, A. X. Falc?ao, C. T. N. Suzuki, and J. F. Gomes, "A data reduction and organization approach for efficient image annotation," in Proceedings of the 28th Annual ACM Symposium on Applied Computing, ser. SAC '13. New York, NY, USA: ACM, 2013, pp. 53-57
16. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag New York, Inc., 1995
17. G. Schohn and D. Cohn, "Less is more: Active learning with support vector machines," in Proceedings of the Seventeenth International Conference on Machine Learning, ser. ICML '00. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2000, pp. 839-846
18. S. Tong and D. Koller, "Support vector machine active learning with applications to text classification," Journal of Machine Learning Research, vol. 2, pp. 45-66, Mar. 2002
19. P. Mitra, B. U. Shankar, and S. K. Pal, "Segmentation of multispectral remote sensing images using active support vector machines," Pattern Recognition Letters, vol. 25, no. 9, pp. 1067-1074, 2004
20. L. Bruzzone and C. Persello, "Active learning for classification of remote sensing images," in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), vol. 3, July 2009, pp. III-693-III-696
21. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 8, pp. 1778-1790, Aug 2004
22. P. E. Rauber, A. X. Falc?ao, T. V. Spina, and P. J. de Rezende, "Interactive segmentation by image foresting transform on superpixel graphs," in Conference on Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI, Aug 2013, pp. 131-138
23. J. A. dos Santos, P.-H. Gosselin, S. Philipp-Foliguet, R. Torres, and A. X. Falc?ao, "Multiscale classification of remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3764-3775, Oct 2012
24. M. J. Swain and D. H. Ballard, "Color indexing," International Journal of Computer Vision, vol. 7, no. 1, pp. 11-32, Nov. 1991
25. T. Ojala, M. Pietikinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996
26. R. O. Stehling, M. A. Nascimento, and A. X. Falc?ao, "A compact and efficient image retrieval approach based on border/interior pixel classification," in Proceedings of the Eleventh International Conference on Information and Knowledge Management, ser. CIKM '02. New York, NY, USA: ACM, 2002, pp. 102-109
27. C. D. Ferreira, J. A. dos Santos, R. da S. Torres, M. A. Gonc?alves, R. C. Rezende, and W. Fan, "Relevance feedback based on genetic programming for image retrieval," Pattern Recogn. Lett., vol. 32, no. 1, pp. 27-37, Jan. 2011
28. D. Tuia, F. Pacifici, M. Kanevski, and W. Emery, "Classification of very high spatial resolution imagery using mathematical morphology and support vector machines," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, pp. 3866-3879, Nov 2009
29. J. Benediktsson, J. Palmason, and J. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, pp. 480-491, March 2005
30. J. Munoz-Mari, D. Tuia, and G. Camps-Valls, "Semisupervised classification of remote sensing images with active queries," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3751-3763, Oct 2012
31. P. T. M. Saito, P. J. de Rezende, A. X. Falc?ao, C. T. N. Suzuki, and J. F. Gomes, "An active learning paradigm based on a priori data reduction and organization," Expert Systems with Applications (ESwA), vol. 41, no. 14, pp. 6086-6097, 2014.