Statistical data analysis models for determining the relevance of structural image descriptions

Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Gadetska, S.; Al-Dhaifallah Mujahed

Публікація:
Statistical data analysis models for determining the relevance of structural image descriptions

dc.contributor.author	Daradkeh Yousef Ibrahim
dc.contributor.author	Gorokhovatskyi, V.
dc.contributor.author	Tvoroshenko, I.
dc.contributor.author	Gadetska, S.
dc.contributor.author	Al-Dhaifallah Mujahed
dc.date.accessioned	2023-12-01T14:51:47Z
dc.date.available	2023-12-01T14:51:47Z
dc.date.issued	2023
dc.description.abstract	The aim of the research is to improve the effectiveness of image recognition methods according to the description in the form of a set of keypoint descriptors. The focus is on increasing the speed of analysis and processing of description data while maintaining the required level of classification efficiency. The class of the image is provided as a description of the etalon. It is proposed to transform the description by implementing a statistical system of features for non-intersecting data fragments. The developed method is based on the aggregation of data distribution values within the description, the basis of which is the bit representation of the descriptors. Statistical features are calculated as the frequency of occurrence of the fixed value of a fragment on a set of description data and thus reflect the individual properties of images. Three main classifier models are analyzed: calculating the measure of data relevance in the form of distributions; assigning each of the descriptors to defined classes (voting); using the apparatus of statistical data analysis to decide on the significance of the difference between the distributions of the object and etalons. The results of software modeling of methods and calculations of statistical significance of differences based on distributions for training sets of images are represented. Using distributions instead of a set of descriptors increases the processing speed by hundreds of times, while the classification accuracy is maintained at a sufficient level and does not deteriorate compared to traditional voting.
dc.description.sponsorship	This work was supported via funding from Prince Sattam bin Abdulaziz University project number PSAU/2023/R/1444.
dc.identifier.citation	Statistical data analysis models for determining the relevance of structural image descriptions / Y. I. Daradkeh, V. Gorokhovatskyi, I. Tvoroshenko, S. Gadetska, Al-Dhaifallah M. // IEEE Access, 11, pp. 126938-126949.
dc.identifier.other	DOI: 10.1109/ACCESS.2023.3332291
dc.identifier.uri	https://openarchive.nure.ua/handle/document/24913
dc.language.iso	en_US
dc.publisher	IEEE Access
dc.subject	Computer vision
dc.subject	data fragment
dc.subject	descriptor
dc.subject	image classification
dc.subject	keypoint
dc.title	Statistical data analysis models for determining the relevance of structural image descriptions
dc.type	Article
dspace.entity.type	Publication