A technique for analyzing the structure of a website based on data on hypertext links between pages is proposed. An analysis method based on the topology of links between pages was selected. The mathematical model of the website in the form of a web graph is developed. Structural relationships between pages are represented by binary values in the graph adjacency matrix. The problem of clustering is formulated. To analyze the structure of the web graph the clustering method k-means is used. A metric for determining the distance between cluster elements has been introduced. Assessment of the complexity of the algorithm is performed. Website pages correspond to hierarchical units of the structure. The structure distinguishes between pages of categories and subcategories of goods, pages of goods, and thematic articles. Types of site pages are selected as clusters. Typical pages for each cluster are selected as centroids. An iterative algorithm for constructing a web graph has been developed. The queue is selected as the data structure for storing local information when crawling pages. Testing of the proposed approach is carried out on the example of an existing online store. A division of the site pages into clusters was obtained as a result of the analysis. A division is corresponded to hierarchical elements of the structure: product categories, subcategories, product pages. The type of pages that are poorly identified by the algorithm is revealed. Using the results of clustering, you can improve the site structure during reengineering. Application of the developed methodology will improve the indexing of the site in the search engine.
The problem of automatic colorization of monochrome images is considered. methods of colorizing are used in film industry to restore chromaticity of old movies and photographic materials, in computer vision problems, in medical images processing etc. Modern techniques of colorization contain many manual operations, take a lot of time and are expensive. Many methods of colorization require human participation to correctly determine colors, since there is no one-to-one accordance between grayscale and color. In this paper we discuss method for fully automatic colorization of monochrome images using a convolutional neural network. This approach has reduced using of manual operations to minimum. Structure of the neural network for coloration based on the VGG16 model is considered in the paper. Types of layers that are appropriate for solving the problem of colorization are determined and analyzed. Proposed structure consists of 13 convolutional layers and three upsampling layers. The subsample layers are replaced with the necessary zero addition with a step of 2x2. All layers’ filters have 3x3 size. The activation function of all convolutional layers is ReLU and hyperbolic tangent of the last layer. The presented model is implemented in a software system for automatic image colorization. The software system includes two parts. The first part implements construction and training of the neural network. The second part uses obtained neural network to generate colorized images from grayscale images. Network training was carried out on a sample of Caltech-256, which contains 256 categories of objects. After training the system was tested on series of grayscale images. Testing showed that the system performs enough plausible colorization of certain types objects. Acceptable results were obtained in the colorization of images of nature, ordinary animals, portrait photos. In unsuccessful cases objects were painted in brown shades. Unsuccessful results were obtained for images that contained only parts of objects or these objects were represented in the training sample in too different colors.
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