Метод максимально правдоподобного перебора в задаче классификации кусочно-однородных объектов
The problem of the choice of algorithms parameters in automatic image recognition is put and solved by ensemble classifiers construction using the maximum posterior probability principle. The new criterion of parameters choice is strictly synthesized for Kullback-Leibler information discrimination and modern SIFT (Scale-Invariant Feature Transform) method of object recognition. The program and results of experimental research in a problem of face recognition with widely used databases (Yale, AT&T) are presented. It is shown that the proposed criterion allows to achieve recognition accuracy equal to the algorithm with the best parameters set, and not only for Kullback-Leibler information discrimination, but also for other popular distances (Euclidean metric, Kullback information divergence).
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).
A new modification of the method of directed alternatives' enumeration using the Kullback-Leibler discrimination information is proposed for half-tone image recognition.Results of an experimental studyin the problem of face images recognition with a large database are pre-sented. It is shown that the proposed modification is characterized by increased speed of image recognition (5-10 times vs exhaustive search).
The problem of automatic image recognition based on the minimum information discrimination principle is formulated and solved. Color histograms comparison in the Kullback–Leibler information metric is proposed. It’s combined with method of directed enumeration alternatives as opposed to complete enumeration of competing hypotheses. Results of an experimental study of the Kullback-Leibler discrimination in the problem of face recognition with a large database are presented. It is shown that the proposed algorithm is characterized by increased accuracy and reliability of image recognition.
The problem of management of the nonlinear object which is exposed to impact of uncontrollable indignations, is considered in a key of differential game. Synthesis of optimum managements is made with application of transformation of the nonlinear equation of initial object in the differential equation with the parameters depending on a condition. The square-law functional of quality allows to formulate synthesis conditions in the form of need of search of solutions of the equation of Rikkati. The solution of the equation of Rikkati with the parameters depending on a condition, is in a symbolical view with application of algebraic methods that allows to generalize a number of earlier published theoretical results, to receive rather constructive decisions in a number of statements of problems of management.
The article is based upon the fact that the growing demand for master data management systems has not yet produced a commonly accepted metodology for their design and development/ The article offers two mathematical models? that allow a master data management systems designer a way to formally describe their system before development and verify the system quality by measurements? unique to master data management systems.