Projects per year
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  by 6.4% and 3.8% respectively on artist and style classification.
|Title of host publication||ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval|
|Number of pages||4|
|Publication status||Published - 6 Jun 2016|
|MoE publication type||A4 Article in a conference publication|
|Event||ACM International Conference on Multimedia Retrieval - New York, United States|
Duration: 6 Jun 2016 → 9 Jun 2016
Conference number: 6
|Conference||ACM International Conference on Multimedia Retrieval|
|Period||06/06/2016 → 09/06/2016|
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- 1 Finished
Xu, Y., Rintanen, J., Kaski, S., Anwer, R., Parviainen, P., Soare, M., Vuollekoski, H., Rezazadegan Tavakoli, H., Peltola, T., Blomstedt, P., Puranen, S., Dutta, R., Gebser, M., Mononen, T., Bogaerts, B., Tasharrofi, S., Pesonen, H., Weinzierl, A. & Yang, Z.
01/01/2015 → 31/12/2017
Project: Academy of Finland: Other research funding