Abstract
An incremental approach to the discriminative common vector (DCV) method for image recognition is considered. Discriminative projections are tackled in the particular context in which new training data becomes available and learned subspaces may need continuous updating. Starting from incremental eigendecomposition of scatter matrices, an efficient updating rule based on projections and orthogonalization is given. The corresponding algorithm has been empirically assessed and compared to its batch counterpart. The same good properties and performance results of the original method are kept but with a dramatic decrease in the computation needed.
Work partially funded by FEDER and Spanish and Valencian Governments through projects TIN2009-14205-C04-03, ACOMP/2010/287, GV/2010/086 and Consolider Ingenio 2010 CSD07-00018.
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Diaz-Chito, K., Ferri, F.J., Díaz-Villanueva, W. (2011). Null Space Based Image Recognition Using Incremental Eigendecomposition. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_39
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DOI: https://doi.org/10.1007/978-3-642-21257-4_39
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