Nonnegative complementary prototype representation based classifier for object recognition

Based on the assumption that a query image can be represented as the nonnegative combination of the generated class-specific prototypes, a simple but effective classifier, non-negative complementary prototype representation based classifier (NCPRC), is proposed for object recognition. First, the query-dependent class prototypes are constructed using least squares. Second, we apply nonnegative least squares to estimate the coefficients of the prototypes. Finally, the identity of a query image is disclosed by the farthest rule with complementary prototypes. The proposed classifier doesn´t need the delicate parameter tuning. Experiments on four standard image datasets validate the superiority of our approach over several state-of-the-art methods.