Gručenje umetniških slik na podlagi njihovih značilnic

diplomsko delo

Abstract

V tej diplomski nalogi želimo preizkusiti metodo, ki nam omogoči, da s pomočjo računalniške analize sliko pripišemo določenemu slikarju. Testiramo dva načina. Pri prvem pristopu želimo identificirati slikarja glede na način, s katerim preslika človeške obrazne poteze iz fotografije na naslikan portret. Zanima nas, ali so razlike v obraznih razmerjih na fotografiji in sliki statistično pomembne. Pri drugi metodi vsako sliko opišemo z vektorjem značilnic. Značilnice obsegajo barvo, teksturo in dimenzije slike, katerih kombinacija tvori vektor značilnic. Princip testiramo na 3 slikarjih z različnimi stili. Za vsakega od njih imamo množico desetih testnih slik. Zanima nas, ali lahko z gručenjem sliko pravilno pripišemo slikarju samo na podlagi teh vektorjev značilnic.

Keywords

računalniški vid;umetnost;detekcija obraza;primerjava umetniških slik;klasifikacija;klasifikacija umetnikov;računalništvo;računalništvo in informatika;računalništvo in matematika;univerzitetni študij;diplomske naloge;interdisciplinarni študij;

Data

Language:	Slovenian
Year of publishing:	2015
Typology:	2.11 - Undergraduate Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[N. Vesel]
UDC:	004.932:75(043.2)
COBISS:	1536458179
Views:	1711
Downloads:	382
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Artwork classification based on image features
Secondary abstract:	In this thesis we are trying to discover a method that allows us to attribute a painting to a particular artist with the help of image analysis. We are testing two methods. In the first one, we are trying to identify the style of a painter by analysing the way in which he translates a human face from a photograph into a painting. We are testing whether the differences on facial proportions in photographs and paintings are statistically significant. With the other method, we describe every painting with a set of features. The features look at the image color, texture and dimensions to form a feature vector. We test this on 10 pictures for each of the 3 painters with different styles. We are trying to test, whether we can correctly attribute these paintings to a painter just with these feature vectors.
Secondary keywords:	computer vision;art;face detection;artwork comparison;classification;artist classification;computer science;computer and information science;computer science and mathematics;diploma;interdisciplinary studies;
File type:	application/pdf
Type (COBISS):	Bachelor thesis/paper
Study programme:	1000407
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	52 str.
ID:	8889380