OPF-MRF: Optimum-Path Forest and Markov Random Fields for Contextual-Based Image Classification

1. Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A.: SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 7(4), 736–740 (2010)

   Article  Google Scholar 

2. Moser, G., Serpico, S.B.: Combining support vector machines and markov random fields in an integrated framework for contextual image classification. IEEE Transactions on Geoscience and Remote Sensing PP(99), 1–19 (2012)

   Google Scholar 

3. Wu, T., Bae, M.H., Zhang, M., Pan, R., Badea, A.: A prior feature SVM-MRF based method for mouse brain segmentation. NeuroImage 59(3), 2298–2306 (2012)

   Article  Google Scholar 

4. Besag, E.: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society B36, 192–236 (1974)

   MathSciNet  Google Scholar 

5. Geman, S., Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transaction on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)

   Article  MATH  Google Scholar 

6. Seheult, A.H., Greig, D.M., Porteous, B.T.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society 51(2), 271–279 (1989)

   Google Scholar 

7. Papa, J.P., Falcão, A.X., Suzuki, C.T.N.: Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology 19(2), 120–131 (2009)

   Article  Google Scholar 

8. Papa, J.P., Falcão, A.X., Albuquerque, V.H.C., Tavares, J.M.R.S.: Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition 45(1), 512–520 (2012)

   Article  Google Scholar 

9. Spina, T.V., de Miranda, P.A.V., Falcão, A.X.: Intelligent understanding of user interaction in image segmentation. Intl. Journal of Pattern Recognition and Artificial Intelligence, 26(2), 65001-1–1265001-26 (2012)

   Google Scholar 

10. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. The MIT Press (2001)

    Google Scholar 

11. Allène, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R.: Some links between min-cuts, optimal spanning forests and watersheds. In: Proceedings of the International Symposium on Mathematical Morphology, pp. 253–264. MCT/INPE (2007)

    Google Scholar 

12. Potts, R.B.: Some generalized order-disorder transformations. Mathematical Proceedings of the Cambridge Philosophical Society 48, 106–109 (1952)

    Article  MathSciNet  MATH  Google Scholar 

13. Wu, F.Y.: The potts model. Reviews of Modern Physics 54, 235–268 (1982)

    Article  MathSciNet  Google Scholar 

14. Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B (Methodological) 48(3), 259–302 (1986)

    MathSciNet  MATH  Google Scholar 

15. Cappabianco, F.A.M., Falcão, A.X., Yasuda, C.L., Udupa, J.K.: Brain tissue mr-image segmentation via optimum-path forest clustering. Computer Vision and Image Understanding 116(10), 1047–1059 (2012)

    Article  Google Scholar 

16. Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37 (1960)

    Article  Google Scholar 

Download references