Aggregate Parametric Representation of Image Structural Description in Statistical Classification Methods
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.
Computer vision, key point, data aggregation, metric classifier
Gadetska S., Gorokhovatskyi V., Stiahlyk N., Vlasenko N. (2022) Aggregate Parametric Representation of Image Structural Description in Statistical Classification Methods. In CEUR Workshop Proceedings: Computer Modeling and Intelligent Systems (CMIS-2022), 3137, pp. 68-77. Available online: http:// http://ceur-ws.org/Vol-3137/