Image Recognition through Incremental Discriminative Common Vectors

An incremental approach to the discriminative common vector (DCV) methodfor imagerecognition is presented. Two different but equivalent ways of computing both common vectors and corresponding subspace projections have been considered in the particular context in which new training data becomes available and learned subspaces may need continuous updating. The two algorithms are based on either scatter matrixeigendecomposition or difference subspace orthonormalizationas with the original DCV method. The proposed incremental methods keep the same good properties than the original one but with a dramatic decrease in computational burden when used in this kind of dynamic scenario. Extensive experimentation assessing the properties of the proposed algorithms using several publicly available image databases has been carried out.