Information-extreme machine training of on-board recognition system with optimization of RGB-component digital images

Abstract

The research increases the recognition reliability of ground natural and infrastructural objects by use of an autonomous onboard unmanned aerial vehicle (UAV). An information-extreme machine learning method of an autonomous onboard recognition system with the optimization of RGB components of a digital image of ground objects is proposed. The method is developed within the framework of the functional approach to modeling cognitive processes of natural intelligence at the formation and acceptance of classification decisions. This approach, in contrast to the known methods of data mining, including neuro-like structures, provides the recognition system with the properties of adaptability to arbitrary initial conditions of image formation and flexibility in retraining the system. The idea of the proposed method is to maximize the information capacity of the recognition system in the machine learning process. As a criterion for optimizing machine learning parameters, a modified Kullback information measure was used, this informational criterion is the functionality of exact characteristics. As optimization parameters, the geometric parameters of hyperspherical containers of recognition classes and control tolerances for recognition signs were considered, which played the role of input data quantization levels when transforming the input Euclidean training matrix into a working binary training matrix using admissible transformations of a working training matrix the offered machine learning method allows to adapt the input mathematical description of recognition system to the maximum full probability of the correct classification decision acceptance. To increase the depth of information-extreme machine learning, optimization was conducted according to the information criterion of the weight coefficients of the RGB components of the brightness spectrum of ground object images. The results of physical modeling on the example the recognition of terrestrial natural and infrastructural objects confirm the increase in functional efficiency of information-extreme machine learning of on-board system at optimum in information understanding weight coefficients of RGB-components of terrestrial objects image brightness.

Decision-making support system for diagnosis of oncopathologies by histological images

“…At the same time, among the additional optimization parameters, the parameters of forming the input information description of the diagnostic DSS will play an important role. Such machine learning parameters can be, for example, the the image frame size, the RGB components weights of the histological image 12 and so on. In addition, a promising way to increase the reliability of histological image segmentation is to assess the informativeness of diagnostic features by the method of information-extreme machine learning, proposed in the authors work.…”

“…One of the promising approaches to the information synthesis of diagnostic DSS is the use ideas and methods of so-called information-extreme intelligent technology (IEIT) data analysis, which is based on maximizing the information capacity of the system in machine learning. 10 , 11 , 12 The idea of IEIT methods, as in CNN, is to adapt the input mathematical description in the machine learning to the maximum possible probability of making the correct diagnostic decisions. But the main advantage of information-extreme machine learning methods is that, unlike neuro-like structures, they are developed as part of a functional approach to modeling cognitive processes inherent in man in the formation and adoption of classification decisions.…”

“…It should be emphasized that the information-extreme machine learning methods are not interactive training matrix is an order of magnitude smaller than the image samples. In Naumenko, 12 an example of information synthesis within the IEIT diagnostic DSS for 4 recognition classes is given. But since the linear information-extreme machine learning algorithm was implemented, it was not possible to build highly reliable decisive rules.…”