Multi-label Poster Classification into Genres Using Different Problem Transformation Methods (CROSBI ID 654185)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Pobar, Miran ; Ivašić-Kos, Marina
engleski
Multi-label Poster Classification into Genres Using Different Problem Transformation Methods
Classification of movies into genres from the accompanying promotional materials such as posters is a typical multi-label classification problem. Posters usually highlight a movie scene or characters, and at the same time should inform about the genre or the plot of the movie to attract the potential audience, so our assumption was that the relevant information can be captured in visual features. We have used three typical methods for transforming the multi-label problem into a number of single-label problems that can be solved with standard classifiers. We have used the binary relevance, random k-labelsets (RAKEL), and classifier chains with Naïve Bayes classifier as a base classifier. We wanted to compare the classification performance using structural features descriptor extracted from poster images, with the performance obtained using the Classeme feature descriptors that are trained on general images datasets. The classification performance of used transformation methods is evaluated on a poster dataset containing 6000 posters classified into 18 and 11 genres.
Multi-label classification RAKEL ensemble method Binary relevance Classifier chains Movie poster Classemes
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Podaci o prilogu
367-378.
2017.
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objavljeno
10.1007/978-3-319-64698-5_31
Podaci o matičnoj publikaciji
Lecture notes in computer science
Felsberg, Michael ; Heyden, Anders ; Krüger, Norbert
Ystad: Springer
978-3-319-64697-8
0302-9743
1611-3349
Podaci o skupu
CAIP 2017
predavanje
22.08.2017-24.08.2017
Ystad, Švedska
Povezanost rada
Trošak objave rada u otvorenom pristupu
Računarstvo, Informacijske i komunikacijske znanosti