20.500.12556/RUL-29528 A Markov random field based autonomous image segmentation bachelor's thesis Avtonomna segmentacija slik z Markovim slučajnim poljem Image segmentation is a widely-researched topic with many algorithms available. Our goal is to segment an image, in an unsupervised way, into several coherent parts with the help of superpixels. To achieve that, we propose an iterative segmentation algorithm. The algorithm models the image by a Markov random field, whose nodes are the superpixels, and each node has both color and texture features. The superpixels are assigned labels according to their features with the help of support vector machines and the aforementioned MRF and the number of segments is iteratively reduced. The result is a segmentation of an image into several regions with requiring any user input. The segmentation algorithm was tested on a standard evaluation database, and performs on par with state-of-the-art segmentation algorithms in F-measures. In terms of oversegmentation, our approach significantly outperforms the state of the art by greatly reducing the oversegmentation of the object of interest. Segmentacija slik je zelo raziskovano področje, za katero so na voljo številni algoritmi. Naš cilj je segmentacija slike s pomočjo superpikslov na več skladnih delov in na nenadzorovan način. Da bi to dosegli, predlagamo iterativni segmentacijski algoritem. Algoritem predstavlja sliko kot slučajno polje Markova (MRF), katerega vozlišča so superpiksli, ki imajo barvne in teksturne atribute. Superpikslom dodelimo oznake na podlagi njihovih atributov s pomočjo metode podpornih vektorjev (SVM) in že omenjenega MRF in iterativno zmanjšujemo število segmentov. Negotovo segmentacijo po vsaki iteraciji se izboljšuje in rezultat je segmentacija slike na več semantično smiselnih delov, brez pomoči uporabnika. Algoritem je bil testiran na segmentacijsko podatkovno bazo in F ocene so podobne najsodobnejšim algoritmom. Glede fragmentacije slike naš pristop bistveno prekosi stanje tehnike z zmanjšanjem števila segmentov, iz katerih je sestavljen predmet zanimanja. segmentation support vector machines SVM Markov random field MRF unsupervised learning computer science computer and information science diploma segmentation support vector machines SVM Markov random segmentacija metoda podpornih vektorjev SVM slučajno polje Markova MRF nenadzorovano učenje računalništvo računalništvo in informatika univerzitetni študij diplomske naloge true false false [A. Dimitriev] Angleški jezik Slovenski jezik Diplomsko delo/naloga 2014-09-19 17:55:05 2014-09-19 17:55:06 2024-08-12 15:10:32 0000-00-00 00:00:00 2014 0 0 54 str. 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 13960 1536081859 63110395-20140912122016-Dimitriev1.pdf 63110395-20140912122016-Dimitriev1.pdf 1 F895648233F892A162579289AE87BC4D ea7199089d924e1eef36c176387360bdeaa4f271f5e5947498ed480b8600595c 4ec6bb63-a1a8-11eb-a523-00155dcfd717 20.500.12556/rul/afc6ae51-fe0c-48f6-bd0e-0bf43da5e918 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=29524 Fakulteta za računalništvo in informatiko 0 0 0