Cluster representation of the structural description of images for effective classification

Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Zeghid, M.

Публікація:
Cluster representation of the structural description of images for effective classification

dc.contributor.author	Daradkeh Yousef Ibrahim
dc.contributor.author	Gorokhovatskyi, V.
dc.contributor.author	Tvoroshenko, I.
dc.contributor.author	Zeghid, M.
dc.date.accessioned	2022-08-04T21:24:35Z
dc.date.available	2022-08-04T21:24:35Z
dc.date.issued	2022
dc.description.abstract	The problem of image recognition in the computer vision systems is being studied. The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided. We propose three versions of the classifier according to the following principles: “object–etalon”, “object descriptor–etalon” and “vector description of the object–etalon”, which are not similar in level of integration of researched data analysis. The options for constructing clusters over the whole set of descriptions of the etalon database, separately for each of the etalons, as well as the optimal method to compare sets of segment centers for the etalons and object, are implemented. An experimental rating of the efficiency of the created classifiers in terms of productivity, processing time, and classification quality has been realized of the applied. The proposed methods classify the set of etalons without error. We have formed the inference about the efficiency of classification approaches based on segment centers. The time of image processing according to the developed methods is hundreds of times less than according to the traditional one, without reducing the accuracy.	uk_UA
dc.identifier.citation	Daradkeh Y.I., Gorokhovatskyi V., Tvoroshenko I., and Zeghid M. (2022) Cluster representation of the structural description of images for effective classification, Computers, Materials & Continua, vol. 73, no. 3, pp. 6069–6084.	uk_UA
dc.identifier.other	DOI: 10.32604/cmc.2022.030254
dc.identifier.uri	https://openarchive.nure.ua/handle/document/20820
dc.language.iso	en	uk_UA
dc.publisher	Computers, Materials & Continua	uk_UA
dc.subject	Cluster representation	uk_UA
dc.subject	computer vision	uk_UA
dc.subject	description relevance	uk_UA
dc.subject	descriptor	uk_UA
dc.subject	image classification	uk_UA
dc.subject	keypoint	uk_UA
dc.subject	processing speed	uk_UA
dc.subject	vector space	uk_UA
dc.title	Cluster representation of the structural description of images for effective classification	uk_UA
dc.type	Article	uk_UA
dspace.entity.type	Publication