The iMet Collection 2019 Challenge Dataset

Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification. In this work, we present a novel dataset for fine-grained artwork attribute recognition. The images in the dataset are professional photographs of classic artworks from the Metropolitan Museum of Art, and annotations are curated and verified by world-class museum experts. In addition, we also present the iMet Collection 2019 Challenge as part of the FGVC6 workshop. Through the competition, we aim to spur the enthusiasm of the fine-grained visual recognition research community and advance the state-of-the-art in digital curation of museum collections.

[1]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Jitendra Malik,et al.  Detecting People in Cubist Art , 2014, ECCV Workshops.

[3]  Hailin Jin,et al.  BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[4]  Andrew Zisserman,et al.  In Search of Art , 2014, ECCV Workshops.

[5]  Tat-Seng Chua,et al.  Analysis and Retrieval of Paintings Using Artistic Color Concepts , 2005, ICME.

[6]  F. Precioso,et al.  3D Content-Based Retrieval in Artwork Databases , 2007, 2007 3DTV Conference.

[7]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[8]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.