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2018 24th International Conference on Pattern Recognition (ICPR) (2018)
Beijing, China
Aug. 20, 2018 to Aug. 24, 2018
ISSN: 1051-4651
ISBN: 978-1-5386-3789-0
pp: 3704-3709
Lukas Tuggener , ZHAW Datalab & USI
Ismail Elezi , University of Venice & ZHAW
Jurgen Schmidhuber , IDSIA & USI
Marcello Pelillo , University of Venice
Thilo Stadelmann , ZHAW Datalab
ABSTRACT
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small object recognition by placing the question of object recognition in the context of scene understanding. DeepScores contains high quality images of musical scores, partitioned into 300, 000 sheets of written music that contain symbols of different shapes and sizes. With close to a hundred million small objects, this makes our dataset not only unique, but also the largest public dataset. DeepScores comes with ground truth for object classification, detection and semantic segmentation. DeepScores thus poses a relevant challenge for computer vision in general, and optical music recognition (OMR) research in particular. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets. Finally, we provide baseline performances for object classification, intuition for the inherent difficulty that DeepScores poses to state-of-the-art object detectors like YOLO or R-CNN, and give pointers to future research based on this dataset.
INDEX TERMS
Music, Computer vision, Task analysis, Image segmentation, Object detection, Semantics, Optical character recognition software
CITATION

L. Tuggener, I. Elezi, J. Schmidhuber, M. Pelillo and T. Stadelmann, "DeepScores-A Dataset for Segmentation, Detection and Classification of Tiny Objects," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018, pp. 3704-3709.
doi:10.1109/ICPR.2018.8545307
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