Efficient deep features selections and classification for flower species recognition

dc.contributor.author	Cibuk, Musa
dc.contributor.author	Budak, Umit
dc.contributor.author	Guo, Yanhui
dc.contributor.author	Ince, M. Cevdet
dc.contributor.author	Sengur, Abdulkadir
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2019
dc.identifier.issn	0263-2241
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10116
dc.identifier.uri	https://doi.org/10.1016/j.measurement.2019.01.041
dc.description.abstract	Image-based automatic flower species classification is an important problem for the biologists who construct digital flower catalogs. A dozen of work about flower species recognition has been proposed so far based on traditional image processing routines.
dc.language.iso	English
dc.publisher	Elsevıer Scı Ltd
dc.source	Measurement
dc.title	Efficient deep features selections and classification for flower species recognition
dc.type	Article
dc.identifier.startpage	7
dc.identifier.endpage	13
dc.identifier.doi	10.1016/j.measurement.2019.01.041
dc.identifier.wos	WOS:000464553200002
dc.identifier.volume	137

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