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Pregled bibliografske jedinice broj: 902373

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Autori: Pobar, Miran; Ivašić-Kos, Marina
Naslov: Multi-label Poster Classification into Genres Using Different Problem Transformation Methods
Izvornik: Computer Analysis of Images and Patterns, CAIP 2017, Lecture Notes in Computer Science, vol. 1042 / Felsberg, Michael ; Heyden, Anders ; Krüger, Norbert (ur.). - Ystadu, Švedskoj : Springer , 2017. 367-378 (ISBN: 978-3-319-64697-8).
Skup: CAIP 2017
Mjesto i datum: Ystadu, Švedskoj, 22-24.08.2017
Ključne riječi: Multi-label classification RAKEL ensemble method Binary relevance Classifier chains Movie poster Classemes
Sažetak:
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.
Vrsta sudjelovanja: Predavanje
Vrsta prezentacije u zborniku: Cjeloviti rad (više od 1500 riječi)
Vrsta recenzije: Međunarodna recenzija
Projekt / tema: HRZZ-IP-06-2016-8345
Izvorni jezik: ENG
Kategorija: Znanstveni
Znanstvena područja:
Računarstvo,Informacijske i komunikacijske znanosti
Upisao u CROSBI: Marina Ivašić Kos (Marina.Ivasic@inf.uniri.hr), 31. Lis. 2017. u 15:37 sati



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