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master's thesis
Klasifikacija slika dubokim modelima i Fisherovim vektorima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. urn:nbn:hr:168:084270
Viduka, Ivana
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Viduka, I. (2017). Klasifikacija slika dubokim modelima i Fisherovim vektorima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:084270

Viduka, Ivana. "Klasifikacija slika dubokim modelima i Fisherovim vektorima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:084270

Viduka, Ivana. "Klasifikacija slika dubokim modelima i Fisherovim vektorima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2017. https://urn.nsk.hr/urn:nbn:hr:168:084270

Viduka, I. (2017). 'Klasifikacija slika dubokim modelima i Fisherovim vektorima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:084270

Viduka I. Klasifikacija slika dubokim modelima i Fisherovim vektorima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2017 [cited 2024 December 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:084270

I. Viduka, "Klasifikacija slika dubokim modelima i Fisherovim vektorima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:168:084270

Metadata
TitleKlasifikacija slika dubokim modelima i Fisherovim vektorima
Title (english)Image classification with deep models and Fisher vectors
AuthorIvana Viduka
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberJan Šnajder (član povjerenstva)
Committee memberSlobodan Ribarić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2017-07-17, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Za ovaj rad su opisane i implementirane dvije metode klasifikacije slika. Prvu metodu čini duboki model temeljen na arhitekturi VGG16 konvolucijske mreže čiji su parametri inicijalizirani težinama naučenima na slikama ImageNet baze. Drugi pristup uključuje konvolucijske značajke dobivene uz pomoć javne parametrizacije VGG19 mreže iz kojih se generiraju Fisherovi vektori, a zatim se provodi klasifikacija pllitkim klasifikatorom. Usporedbom rezultata klasifikacije dobivenih učenjem i testiranjem na dva skupa slika koji prikazuju scene s hrvatskih prometnica - Zebre i Znakovi, zaključuje se da end-to-end pristup učenju klasifikatora kakav je prisutan kod konvolucijske mreže donosi puno bolje rezultate.
Abstract (english)
Two different image classification methods are described and implemented for this work. The first method is a deep model based on the architecture of VGG16 convolutional network, having parameters initialized with weights learned on images from ImageNet database. The second approach includes Fisher vectors generated from convolutional features that are extracted with the help of the publicly available pretrained VGG19 network. Shallow classifier is then used for the classification of Fisher vectors. Comparing the classification results obtained for images from two datasets containing traffic scenes of Croatian roads, we draw a conclusion that the end-to-end approach to the image classification, like the one used in our deep model, shows significantly better results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:084270
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-14 01:56:09