Novel general KNN classifier and general nearest mean classifier for visual classification

This paper presents a novel general k nearest neighbour classifier (GKNNc) and a novel general nearest mean classifier (GNMc) for visual classification. Instead of treating the data equally, both GKNNc and GNMc assign a weight coefficient to each data. To achieve good performance, the conditions and properties of the weight coefficients for GKNNc and GNMc are further analysed. Then a sparse representation based method is proposed to derive the weight coefficients for both GKNNc and GNMc. Experimental results on several representative data sets, such as the Caltech 101 dataset and the MIT-67 indoor scenes dataset demonstrate the feasibility of the proposed methods.