THE FORMATION OF FEATURE SPACE FOR CLASSIFICATION PROBLEMS OF COMPLEX STRUCTURED IMAGES BASED ON SPECTRAL WINDOWS AND NEURAL NETWORK STRUCTURES
Self-organizing neural network structures are designed to highlight the segments of a given class in the image. The relevance in the development of intelligent classification systems of complex structured images occurs when we process radiographs. For classification of complex structured images there have been proposed computer technologies based on boosting methodology. The space of informative features is formed by spectral windows obtained by scanning the original image. The spectral windows can be used as an alternative to the primitives of Haar being used for the image classification by Viola-Jones. The spectral windows belonging to different classes are arranged in the form of clusters on the plane of Kohonen. For clusters formation the rules of correcting the vectors of weights of the current example are used being constructed on the Kohonen rule. Initially distance is entered in the plane of Kohonen and the correction of the weight coefficients of clusters is carried out, allowing to reduce the magnitudes of insignificant component vectors, determining the coordinates of the clusters. Based on the cluster structure of the plane of Kohonen, strong classifiers are built. As an example of implementation of strong classifier there has been given the structure of a strong classifier based on neural networks of direct distribution of block type. As an example of implementation of the proposed computer technology there has been considered the task of classifying radiographs of chest with pneumonia.