Directed enumeration method in image recognition

The article is devoted to the problem of image recognition in real-time applications with a large database containing hundreds of classes. The directed enumeration method as an alternative to exhaustive search is examined. This method has two advantages. First, it could be applied with measures of similarity which do not satisfy metric properties (chi-square distance, Kullback–Leibler information discrimination, etc.). Second, the directed enumeration method increases recognition speed even in the most difficult cases which seem to be very important in practical terms. In these cases many neighbors are located at very similar distances. In this paper we present the results of an experimental study of the directed enumeration method with comparison of color- and gradient-orientation histograms in solving the problem of face recognition with well-known datasets (Essex, FERET). It is shown that the proposed method is characterized by increased computing efficiency of automatic image recognition (3–12 times in comparison with a conventional nearest neighbor classifier).

► Directed enumeration method is proposed to improve image recognition performance.
► The method is applied with similarity measures which do not met metric properties.
► The method increases performance in 3–12 times in comparison with nearest neighbor.
► Recognition speed is increased when many neighbors are located at similar distances.