Please use this identifier to cite or link to this item: http://openarchive.nure.ua/handle/document/7248
Title: Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features
Authors: Gorokhovatskyi, Volodymyr
Keywords: computer vision
structural recognition methods
set of characteristic features
descriptor
quantization
competitive learning
Issue Date: 2018
Citation: Gorokhovatskyi O. Analysis of Application of Cluster Descriptions in Space of Characteristic Image Features / O. Gorokhovatskyi, V. Gorokhovatskyi, O.Peredrii // Data. – 2018, 3(4), 52. – doi:10.3390/data3040052. Available online: https://www.mdpi.com/2306-5729/3/4/52
Abstract: Abstract: Structural image recognition method modifications in space of characteristic features for recognition of computer vision image dataset were investigated. Recognition performance boost is achieved with quantization (clustering) in the space of image characteristic features that form the pattern of the object. Due to the transformation of structural objects descriptions from a set representation to a vector form, the amount of computation might be reduced tens of times. The results of experiments on Leeds Butterfly dataset that confirmed the effectiveness of decision-making systems based on the proposed approach are shown.
URI: http://openarchive.nure.ua/handle/document/7248
Appears in Collections:Кафедра інформатики (ІНФ)

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