Перегляд за автором "Stiahlyk, N."

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  • Зображення мініатюри
    Публікація
    Accelerating Image Classification based on a Model for Estimating Descriptor-to-Class Distance
    (2023) Gorokhovatskyi, V.; Gadetska, S.; Stiahlyk, N.
    The article describes a method of image classification based on the estimation of the distance to the etalon class. The implementation of estimates gives a significant gain in classification speed compared to linear search while maintaining a decent level of accuracy. The methodology is based on the use of the triangle inequality for images given by a set of binary vectors as descriptors of the image key points. The evaluation is applied to the "object descriptor – etalon" classification method, which is based on the descriptor voting procedure. An analysis of evaluation options is carried out using the parameters of the etalon sets in the form of a medoid and the closest or farthest points from it. The gain in classification time compared to the traditional method proportionally depends on the number of descriptors in the etalon description. Software simulation of classifiers with the implementation of evaluation shows a gain in speed of 350-450 times for the description of 500 descriptors while maintaining one hundred percent classification accuracy on the training set of similar NFT images. A control sample experiment shows that the classifier with estimation can respond better to image details compared to the traditional method.
  • Зображення мініатюри
    Публікація
    Aggregate  Parametric  Representation  of  Image  Structural  Description in Statistical Classification Methods
    (2022) Gadetska, S.; Gorokhovatskyi, V.; Stiahlyk, N.; Vlasenko, N.
    Finding effective classification solutions based on the study of the processed data nature is one of the important tasks in modern computer vision. Statistical distributions are a perfect tool for presenting and analyzing visual data in image recognition systems. They are especially effective when creating new feature spaces, particularly, by aggregating descriptor sets in some appropriate way, including bits. For this purpose, it is natural to apply the number of criteria designed to compare the distribution parameters of the analyzed samples. The article develops a speed-efficient method of image classification by introducing aggregate statistical features for the composition of the description components. The metric classifier is based on the use of statisticalcriteria to assess the significance of the classification decision. The developed classification method based on the aggregation of the feature image set is implemented; the workability of the proposed classifier is confirmed. On the examples of the application of variants ofthe method for the system of the real images features, its effectiveness was experimentally evaluated.
  • Зображення мініатюри
    Публікація
    Image structural classification technologies based on statistical analysis of descriptions in the form of bit descriptor set.
    (2020) Gorokhovatskyi, V.; Gadetska, S.; Stiahlyk, N.
    The problem of image recognition in computer vision systems is considered. We offer technologies for classifying visual objects using a statistical center based on a structural description of the image as a set of key point descriptors. The use of statistics for the bits of the description data helps to increase performance while providing sufficient classification performance. Results of experimental modeling and peculiarities of implementation of the developed approaches are discussed.