Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 860379

Genre Classification of Paintings


Cetinić, Eva; Grgić, Sonja
Genre Classification of Paintings // Proceedings ELMAR-2016 / Muštra, Mario ; Tralić, Dijana ; Zovko-Cihlar, Branka (ur.).
Zagreb: Faculty of Electrical Engineering and Computing, University of Zagreb, 2016. str. 201-204 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 860379 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Genre Classification of Paintings

Autori
Cetinić, Eva ; Grgić, Sonja

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

Izvornik
Proceedings ELMAR-2016 / Muštra, Mario ; Tralić, Dijana ; Zovko-Cihlar, Branka - Zagreb : Faculty of Electrical Engineering and Computing, University of Zagreb, 2016, 201-204

ISBN
978-953-184-221-1

Skup
58th International Symposium Electronics in Marine ELMAR-2016

Mjesto i datum
Zadar, Hrvatska, 12-14.9.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Painting Classification ; Genre ; Image Features ; Visual Art

Sažetak
Extensive digitization efforts in the recent years have led to a large increase of digitized and online available fine-art collections. With digitization of artworks, we aim to preserve all those valuable evidences of various human creative expressions, as well as make them available to a broader audience. The digitalization process of artworks should not constrain only to fulfilling the purpose of preservation, but also serve as a starting point for exploring of this type of data in a novel way, which is made possible with the rise of new achievements in computer vision. In the domain of computer analysis of visual art there are various ongoing research challenges. In this paper, we explore different image feature extraction methods and their applicability in the task of classifying painting by genre. Our dataset includes paintings of various styles grouped in seven genre categories. We achieved an accuracy of 77.57% for the task of genre classification. We concluded that the best performance is achieved when using features derived from a pretrained deep convolutional neural network.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Sonja Grgić (autor)

Avatar Url Eva Cetinić (autor)

Citiraj ovu publikaciju

Cetinić, Eva; Grgić, Sonja
Genre Classification of Paintings // Proceedings ELMAR-2016 / Muštra, Mario ; Tralić, Dijana ; Zovko-Cihlar, Branka (ur.).
Zagreb: Faculty of Electrical Engineering and Computing, University of Zagreb, 2016. str. 201-204 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Cetinić, E. & Grgić, S. (2016) Genre Classification of Paintings. U: Muštra, M., Tralić, D. & Zovko-Cihlar, B. (ur.)Proceedings ELMAR-2016.
@article{article, year = {2016}, pages = {201-204}, keywords = {Painting Classification, Genre, Image Features, Visual Art}, isbn = {978-953-184-221-1}, title = {Genre Classification of Paintings}, keyword = {Painting Classification, Genre, Image Features, Visual Art}, publisher = {Faculty of Electrical Engineering and Computing, University of Zagreb}, publisherplace = {Zadar, Hrvatska} }
@article{article, year = {2016}, pages = {201-204}, keywords = {Painting Classification, Genre, Image Features, Visual Art}, isbn = {978-953-184-221-1}, title = {Genre Classification of Paintings}, keyword = {Painting Classification, Genre, Image Features, Visual Art}, publisher = {Faculty of Electrical Engineering and Computing, University of Zagreb}, publisherplace = {Zadar, Hrvatska} }




Contrast
Increase Font
Decrease Font
Dyslexic Font