Combining holistic and part-based deep representations for computational painting categorization

Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost Van De Weijer, Jorma Laaksonen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

16 Sitaatiot (Scopus)

Abstrakti

Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization. We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification.

AlkuperäiskieliEnglanti
OtsikkoICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval
KustantajaACM
Sivut339-342
Sivumäärä4
ISBN (elektroninen)9781450343596
DOI - pysyväislinkit
TilaJulkaistu - 6 kesäkuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaACM International Conference on Multimedia Retrieval - New York, Yhdysvallat
Kesto: 6 kesäkuuta 20169 kesäkuuta 2016
Konferenssinumero: 6

Conference

ConferenceACM International Conference on Multimedia Retrieval
LyhennettäICMR
MaaYhdysvallat
KaupunkiNew York
Ajanjakso06/06/201609/06/2016

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  • Projektit

    • 1 Päättynyt

    Suomalainen laskennallisen päättelyn huippuyksikkö

    Xu, Y., Rezazadegan Tavakoli, H., Pesonen, H., Puranen, S., Rintanen, J., Kaski, S., Anwer, R., Parviainen, P., Soare, M., Weinzierl, A. & Vuollekoski, H.

    01/01/201528/02/2018

    Projekti: Academy of Finland: Other research funding

    Laitteet

    Science-IT

    Mikko Hakala (Manager)

    Perustieteiden korkeakoulu

    Laitteistot/tilat: Facility

  • Siteeraa tätä

    Anwer, R. M., Khan, F. S., Van De Weijer, J., & Laaksonen, J. (2016). Combining holistic and part-based deep representations for computational painting categorization. teoksessa ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval (Sivut 339-342). ACM. https://doi.org/10.1145/2911996.2912063