Principal component analyses (PCA) has been widely used in reduction of the dimensionality of datasets, classification, feature extraction, etc. It has been combined with many oth...
Abstract. A novel frequency domain technique for image blocking artifact reduction is presented in this paper. For each block, its DC and AC coefficients are recalculated for artif...
George A. Triantafyllidis, Dimitrios Tzovaras, Mic...
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
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 ...
We are interested in the problem of reasoning over very large common sense knowledge bases. When such a knowledge base contains noisy and subjective data, it is important to have ...