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Automatic Movie Posters Classification into Genres


Ivašić-Kos, Marina; Pobar, Miran; Ipšić, Ivo
Automatic Movie Posters Classification into Genres // Advances in Intelligent Systems and Computing 311 / Madevska Bogdanova, Ana ; Gjorgjevikj, Dejan (ur.).
Ohrid, Makedonija: Springer, 2014. str. 319-328 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Automatic Movie Posters Classification into Genres

Autori
Ivašić-Kos, Marina ; Pobar, Miran ; Ipšić, Ivo

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Advances in Intelligent Systems and Computing 311 / Madevska Bogdanova, Ana ; Gjorgjevikj, Dejan - : Springer, 2014, 319-328

ISBN
978-3-319-09878-4

Skup
ICT Innovations 2014

Mjesto i datum
Ohrid, Makedonija, 9-12.9.2014.

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Multi-label classification; data transformation method; movie poster

Sažetak
A person can quickly grasp the movie genre (drama, comedy, cartoons, etc.) from a poster, regardless of short observation time, clutter and variety of details. Bearing this in mind, it can be assumed that simple properties of a movie poster should play a significant role in automated detection of movie genres. Therefore, visual features based on colors and structural cues are extracted from poster images and used for poster classification into genres. A single movie may belong to more than one genre (class), so the poster classification is a multi-label classification task. To solve the multi-label problem, three different types of classification methods were applied and described in this paper. These are: ML-kNN, RAKEL and Naïve Bayes. ML-kNN and RAKEL methods are directly used on multi-label data. For the Naïve Bayes the task is transformed into multiple single-label classifications. Obtained results are evaluated and compared on a poster dataset using different feature subsets. The dataset contains 6000 posters advertising films classified into 18 genres. The paper gives insights into the properties of the discussed multi-label clas-sification methods and their ability to determine movie genres from posters using low-level visual features.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekt / tema
318-0361935-0852 - Govorne tehnologije (Ivo Ipšić, )

Ustanove
Sveučilište u Rijeci - Odjel za informatiku