Learning the relevant image features with multiple kernels

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 2, Issue , 2009, Pages

  Tuia, Devis ^a   Matasci, Giona ^a   Camps Valls, Gustavo ^b   Kanevski, Mikhail ^a  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Image classification; Kernel alignment; Multiple kernel learning (MKL); SimpleMKL; Support vector machine (SVM)

Indexed keywords

AUTOMATIC OPTIMIZATION; GRADIENT DESCENT; HYPERSPECTRAL IMAGE CLASSIFICATION; IMAGE FEATURES; LINEAR COMBINATIONS; MODEL SELECTION PROCEDURES; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OBJECTIVE FUNCTIONS; REMOTE SENSING IMAGES; SIMPLEMKL; SPECTRAL CLASSIFICATION; TEXTURAL FEATURE;

ALIGNMENT; GEOLOGY; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RECONSTRUCTION; OPTIMIZATION; REMOTE SENSING;

SUPPORT VECTOR MACHINES;

EID: 77951095824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2009.5418002     Document Type: Conference Paper

References (12)

1. G. Camps-Valls, L. Gómez-Chova, J. Calpe, and E. Soria, "Robust support vector method for hyperspectral data classification and knowledge discovery," IEEE Transactions on Geoscience and Remote Sensing, vol.42 (7), pp. 1530-1542, 2004.
2. Camps-Valls G. Bruzzone L. Kernel-based methods for hyperspectral image classification IEEE Transactions on Geoscience and Remote Sensing 2005 43 6 1351-1362 DOI 10.1109/TGRS.2005.846154 [2] G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. on Geosc. Rem. Sens., vol.43, no.6, pp. 1351-1362, 2005. (Pubitemid 40811944)
3. G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geoscience and Remote Sensing Letters, vol.3, no.1, pp. 93-97, Jan 2006.
4. G. Camps-Valls, L. Gómez-Chova, M. Mũnoz Marí, J.and Martínez-Ramón, and J. L. Rojo-Álvarez, "Kernel-based framework for multi-temporal and multi-source remote sensing data classification and change detection," IEEE Trans. on Geosc. Rem. Sens., vol.46, no.6, pp. 1822-1835, Jun 2008.
5. G. Camps-Valls, "New machine-learning paradigm provides advantages for remote sensing," SPIE Newsroom, July 2008.
6. A. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artificial Intelligence, vol.97, pp. 245-271, 1998.
7. R. Kohavi and G. H. John, "Wrappers for features subset selection," Int J Digit Libr, vol.1, pp. 108-121, 1997.
8. G. Lancricket, T. De Bie, N. Cristianini, M. Jordan, and W. Noble, "A statistical framework for genomic data fusion," Bioinformatics, vol.20, pp. 2626-2635, 2004.
9. S. Sonnenburg, C. Schaefer G. Rätsch, and B. Schölkopf, "Large scale multiple kernel learning," Journal of Machine Learning Research, vol.7, pp. 1531-1565, 2006.
10. A. Villa, M. Fauvel, J. Chanussot, P. Gamba, and J. A. Benediktsson, "Gradient optimization for multiple kernel parameters in support vector machines classification," in Proc. of Geosc. Rem. Sens. Symp., IGARSS08, 2008.
11. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor, "On kernel target alignment," Tech. Rep. 2001-2087, Neuro-COLT, 2001.
12. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "Simple MKL," Journal of Machine Learning Research, vol.9, pp. 2491-2521, 2008.