Image Recognition using SVM-weighted Non-negative Matrix Factorization

This paper presents a new image classification method by learning with non-negative matrix factorization (NMF) and SVM. Firstly, NMF is utilized to extract effective features from the high dimensional feature vector. Then the weight coefficients of features are estimated automatically using relevance feedback strategy by linear SVM. NMF and SVM construct a neural network actually. Finally, classification depends on the K-nearest neighbor rule. Experimental results on the ORL face database and the 9-class task of cells from blood smears show high classification accuracy of the method.