Abstract
Feature selection and dimensionality reduction are crucial research fields in pattern recognition. This work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure with the multispectral bands. Moreover, a criterion function is used to choose automatically the number of final clusters. Experimental results show that the method provides a very suitable subset of multispectral bands for pixel classification purposes.
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References
Beaulieu, J.-M., Goldberg, M.: Hierarchy in picture segmentation: A stepwise optimization approach. IEEE Transactions on PAMI 11, 150–163 (1989)
Bruzzonne, L., Roli, F., Serpico, S.B.: An extension to multiclass cases of the jeffreys-matusita distance. IEEE Trans. on GRS 33, 1318–1321 (1995)
Dhillon, I., Mallela, S., Kumar, R.: A divisive information-theoretic feature clustering algorithm for text classification. JMLR 3, 1265–1287 (2003)
Ding, C., He, X.: Cluster merging and splitting in hierarchical clustering algorithms. In: ICDM 2002, vol. 1, pp. 139–146 (2002)
Dosil, R., Fdez-Vidal, X.R., Pardo, X.M.: Dissimilarity measures for visual pattern partitioning. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 287–294. Springer, Heidelberg (2005)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Jimenez, L., Landgrebe, D.: Supervised classification in high dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data. IEEE Transactions on System, Man, and Cybernetics 28 Part C(1), 39–54 (1998)
Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: In Proceedings of 7th European Conference on Machine Learning, Catania, Italy, pp. 171–182 (1994)
Kumar, S., Ghosh, J., Crawford, M.M.: Best basis feature extraction algorithms for classification of hyperspectral data. IEEE Trans on GRS 39(7), 1368–1379 (2001)
Sharon, E., Brandt, A., Basri, R.: Fast multiscale image segmentation. CVPR 1, 70–77 (2000)
Tourssari, G.D., Frederick, E.D., Markey, M.K., Floyd Jr., C.E.: Applications of mutual information criterion for feature selection in computer-aided diagnosis. Machine Learning Research 3, 2394–2402 (2001)
Vailaya, A., Figueiredo, M., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10, 117–130 (2001)
Ward, J.H.: Hierarchical grouping to optimize an objective function. American Statistical Association 58(301), 236–244 (1963)
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Martínez-Usó, A., Pla, F., García-Sevilla, P., Sotoca, J.M. (2006). Automatic Band Selection in Multispectral Images Using Mutual Information-Based Clustering. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_67
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DOI: https://doi.org/10.1007/11892755_67
Publisher Name: Springer, Berlin, Heidelberg
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